Targets for use in diagnosis, prognosis and therapy of cancer

ABSTRACT

Provided herein are targets that can be used for the diagnosis, prognosis and therapy of a variety of cancers.

RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/US2007/015265, which designated the United States and was filed onJun. 28, 2007, published in English, which claims the benefit of U.S.Provisional Application No. 60/817,249, filed on Jun. 28, 2006, U.S.Provisional Application No. 60/843,271, filed on Sep. 8, 2006, U.S.Provisional Application No. 60/874,409 filed on Dec. 12, 2006 and U.S.Provisional Application No. 60/928,796 filed on May 11, 2007. The entireteachings of the above applications are incorporated herein byreference.

GOVERNMENT SUPPORT

The invention was supported, in whole or in part, by a grantsP01CA97189-01A2 and P50CA113001-01 from the National Cancer Institute,Bethesda, Md. The Government has certain rights in the invention.

BACKGROUND OF THE INVENTION

In order to improve patient management and identify novel compartmentsfor use in diagnosis, prognosis and therapy of cancer, it is essentialto further advance our understanding of this disease at the etiologiclevel.

SUMMARY OF THE INVENTION

Provided herein are targets (markers) that can be used for thediagnosis, prognosis and therapy of a variety of cancers. The markersidentified herein include miRNAs and their target genes for detection offollicular thyroid carcinoma (FTC); haplotype blocks for detection ofPTEN Hamartoma Tumor Syndrome (PHTS), and loss ofheterozygosity/alleleic imbalance (LOH/AI) for detection of head andneck squamous cell carcinoma (HNSCC) and breast cancer.

Follicular Thyroid Carcinoma

Described herein are methods of detecting follicular thyroid carcinomain an individual. In particular embodiments, the method comprisesdetermining expression of one or more microRNAs (e.g., miR-192, miR-197,miR-346 and a combination thereof) in the individual, compared to acontrol; expression of one or more target genes of the microRNAs, in theindividual compared to a control; or determining a combination ofexpression of the microRNAs and target genes in the individual. Anincreased expression of the microRNA, a decreased expression of thetarget gene or a combination thereof, compared to a control isindicative of follicular thyroid carcinoma in the individual.

In another embodiment, the method comprises distinguishing betweenfollicular thyroid carcinoma and follicular adenoma in an individual. Inthis embodiment, the method comprises determining expression of one ormore microRNAs (e.g., miR-192, miR-197, miR-346 and a combinationthereof) in the individual, compared to a control; expression of one ormore target genes of the microRNAs in the individual compared to acontrol; or expression of a combination of expression of the microRNAsand target genes in the individual. An increased expression of themicroRNAs, a decreased expression of the target genes or a combinationthereof, compared to a control is indicative of follicular thyroidcarcinoma in the individual. Alternatively, a decreased expression ofthe microRNAs, an increased expression of the target genes or acombination thereof compared to a control, is indicative of follicularadenoma in the individual.

Also provided herein are methods of inhibiting proliferation of afollicular thyroid carcinoma cell. The method comprises introducing intothe cell one or more agents which inhibit expression or activity of oneor more microRNAs selected from the group consisting of: miR-192,miR-197, miR-346 and a combination thereof; introducing into the cellone or more agents which enhances expression of one or more target genesof a microRNA selected from the group consisting of: miR-192, miR-197,miR-346 and a combination thereof; or introducing into the cell acombination of the one or more agents. The cells are maintained underconditions in which the one or more agents inhibits expression oractivity of the microRNAs, enhances expression of one or more targetgenes of the microRNAs, or results in a combination thereof, therebyinhibiting proliferation of the follicular thyroid carcinoma cell.

Methods of identifying an agent that can be used to inhibitproliferation of a follicular thyroid carcinoma cell are also provided.The method comprises contacting one or more microRNAs selected from thegroup consisting of: miR-192, miR-197, miR-346 with an agent to beassessed; contacting one or more target genes of one or more microRNAsselected from the group consisting of: miR-192, miR-197, miR-346 with anagent to be assessed; or contacting a combination thereof. If expressionof the microRNAs is inhibited in the presence of the agent; of ifexpression of the target genes is enhanced in the presence of the agent,or a combination thereof occurs in the presence of the agent, then theagent can be used to inhibit proliferation of a follicular thyroidcarcinoma cell.

Also provided herein are methods of identifying an agent that can beused to treat a follicular thyroid carcinoma. The method comprisescontacting one or more microRNAs selected from the group consisting of:miR-192, miR-197, miR-346 with an agent to be assessed; contacting oneor more target genes of one or more microRNAs selected from the groupconsisting of: miR-192, miR-197, miR-346 with an agent to be assessed;or contacting a combination thereof. If expression of the microRNAs isinhibited in the presence of the agent; of if expression of the targetgenes is enhanced in the presence of the agent, or a combination thereofoccurs in the presence of the agent, then the agent can be used toinhibit proliferation of a follicular thyroid carcinoma cell.

The invention is also directed to kits for detecting follicular thyroidcarcinoma in an individual comprising one or more reagents for detectingone or more microRNAs selected from the group consisting of: miR-192,miR-197, miR-346 in the individual, compared to a control; one or moretarget genes of one or more microRNAs selected from the group consistingof: miR-192, miR 97, miR-346, in the individual compared to a control;or a combination thereof.

PTEN Hamartoma Tumor Syndrome (PHTS)

PTEN Hamartoma Tumor Syndrome (PHTS) is a heritable cancer syndrome andincludes Cowden Syndrome, Bannayan-Riley-Ruvalcaba Syndrome, ProteusSyndrome, Proteus-Like Syndrome. Described herein is a method ofdiagnosing PHTS or susceptibility to PHTS in an individual comprisingdetecting the presence of at least one haplotype block at theindividual's PTEN locus, wherein the haplotype block is selected fromthe group consisting of a block 1 haplotype, a block 2 haplotype, ablock 3 haplotype and a combination thereof (e.g., extended haplotypes).The presence of one or more of the haplotype blocks is indicative of adiagnosis of PHTS or a susceptibility to PHTS in the individual. Block 1haplotypes, block 2 haplotypes, block 3 haplotypes and combinationsthereof are provided herein, for example, in Tables 9 and 10. In themethods of the present invention, the individual can be PTEN mutationnegative, PTEN mutation positive or PTEN variation positive.

The present invention is also directed to a method of diagnosing PHTS orsusceptibility to PHTS in an individual that is PTEN mutation negativecomprising detecting the presence of at least one haplotype block in thePTEN gene spanning a region upstream of the PTEN gene and the firstintron of the PTEN gene. In a particular embodiment, the haplotype blockin the PTEN gene spans about 33 kb from about position 89,583,605 toabout position 89,616,359 of the genome (e.g., on human chromosome 10).

The present invention also provides kits for use in diagnosing PHTS orsusceptibility to PHTS in an individual comprising one or more reagentsfor detecting one or more haplotype blocks selected from the groupconsisting of: a block 1 haplotype, a block 2 haplotype, a block 3haplotype and a combination thereof.

Head and Neck Squamous Cell Carcinoma (HNSCC)

Described herein are methods of diagnosing head and neck squamous cellcarcinomas (HNSCC) or susceptibility to HNSCC in an individualcomprising detecting the presence of a loss of heterozygosity/allelicimbalance (LOH/AI) at one or more specific loci (markers) in theindividual, wherein the presence of the LOH/AI at the one or morespecific loci in the individual is indicative of a diagnosis of HNSCC inthe individual. In one embodiment, the invention is directed to methodsof diagnosing HNSCC or susceptibility to HNSCC in an individualcomprising detecting the presence of a LOH/AI at one or more lociselected from the group consisting of: D3S3630; D4S2417; D6S305;D18S843; D19S559, in the individual, wherein the presence of the LOH/AIat the one or more loci in the individual is indicative of a diagnosisof HNSCC in the individual. In one embodiment, the one or more of theloci are present in stromal cells (e.g., non-malignant stromal cells,malignant stromal cells) surrounding the tumor (e.g., surrounding tumorepithelial cells), tumor epithelial cells or a combination thereof.

The methods of the present invention can further comprise determiningtumoral attributes, such as aggressiveness of a tumor or disease, extentof HNSCC tumor invasion (e.g., tumor size (pT status), regional lymphnode status (pN; lymph node involvement; lymph node metastasis)), of anHNSCC tumor present in an individual comprising detecting the presenceof a LOH/AI at one or more specific loci in the genome of theindividual.

In a particular embodiment, the invention is directed to a method ofdetecting an aggressive HNSCC tumor in an individual comprisingdetecting the presence of a LOH/AI at one or more specific loci in thegenome of the individual, wherein the presence of the LOH/AI at the oneor more specific loci in the genome of the individual is indicative ofan aggressive HNSCC tumor in the individual.

Also provided herein are kits for use in diagnosing HNSCC orsusceptibility to HNSCC in an individual comprising one or more reagentsfor detecting the presence of a LOH/AI at one or more loci selected fromthe group consisting of: D3S3630; D4S2417; D6S305; D18S843; D19S559.

Breast Cancer

Described herein are methods of diagnosing breast cancer orsusceptibility to breast cancer in an individual comprising detectingthe presence of a loss of heterozygosity/allelic imbalance (LOH/AI) atone or more specific loci (markers) in the individual, wherein thepresence of the LOH/AI at the one or more specific loci in theindividual is indicative of a diagnosis of breast cancer in theindividual. In one embodiment, the invention is directed to methods ofdiagnosing breast cancer or susceptibility to breast cancer in anindividual comprising detecting the presence of a LOH/AI at one or moreloci selected from the group consisting of: D11S1999, D11S1986,ATA42G12, D5S1457, D5S1501, D5S816, D18S858, D20S103, D20S851, D22S683,D22S1045 in the individual, wherein the presence of the LOH/AI at theone or more of eleven specific loci in the individual is indicative of adiagnosis of breast cancer in the individual. In one embodiment, one ormore of the loci are present in the stroma (e.g., non-malignant stroma)surrounding a tumor epithelium and/or epithelial cells of the tumor.

The methods of the present invention can further comprise determiningbreast cancer tumoral attributes, such as aggressiveness of the tumor ordisease, extent of breast tumor invasion (e.g., tumor size (pT status;tumor grade), regional lymph node status (pN; lymph node involvement;lymph node metastasis)), of a breast cancer tumor present in anindividual comprising detecting the presence of a LOH/AI at one or morespecific loci in the genome of the individual.

In a particular embodiment, the invention is directed to a method ofdetecting an aggressive breast cancer tumor in an individual comprisingdetecting the presence of a LOH/AI at one or more specific loci in theindividual, wherein the presence of the LOH/AI at the one or morespecific loci in the individual is indicative of an aggressive breastcancer tumor in the individual.

Also provided herein are kits for use in diagnosing breast cancer orsusceptibility to breast cancer in an individual comprising one or moreregents for detecting the presence of a LOH/AI at one or more lociselected from the group consisting of: D11S1999, D11S1986, ATA42G12,D5S1457, D5S1501, D5S816, D18S858, D20S103, D20S851, D22S683, D22S1045.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIGS. 1A-1C. Quantitative RT-PCR of miR-197 and miR-346 in anindependent set of 5 FTC and 4 FA. (FIG. 1A) Expression levels weredetermined by spot densitometry and normalized to U6 small RNA controls.Normalized density values (Intensity*mm²) are given below each spot.[−RT indicates no-RT negative control] (FIG. 1B) Both miRNAs weresignificantly over-expressed in FTC (black bars) compared to FA (greybars) by 2-fold (*, p<0.0044) and by 1.37-fold (**, p=0.049). (FIG. 1C)Expression of miR-197 and miR-346 in 4 normal Thyroid controls similarto benign thyroid neoplasia.

FIGS. 2A-2C. In vitro over-expression of miR-197 and miR-346 in HEK293Tcells. (FIG. 2A) Expression levels of U6, miR-197 and miR-346 in HEK293Tcells before transfection (0 hours, representing endogenous miR-197 andmiR-346 levels) and at 12 and 24 hours after transfection. (FIG. 2B)Cell growth assay of transfected HEK293T cells. Y-axis representsabsolute viable cell count per experiment, determined by trypan blueexclusion (n=3). * P=0.033, ** P=0.049, ***P=0.003 and ****P=0.012,indicating statistical significant cellular proliferation when comparedto mock transfected controls at the noted time points. (FIG. 2C)Non-viable HEK293T cell count at 8, 12 and 24 hours after transfection.

FIGS. 3A-3C. Functional activity of endogenous miR-197 and miR-346 wasinhibited by transfection of synthetic, chemically modified anti-miR-197and anti-miR-346 oligonucleotides into human follicular thyroid cancercell lines. (FIG. 3A) Growth arrest of FTC-133 cells is observed atsignificant levels after transfection with anti miR-197 (*p=0.0128),anti miR-346 (**p=0.0016) and anti miR-197 together with anti miR-346(****p=0.0026) in comparison to the mock transfected control. (FIG. 3B)In K5 human FTC cells, a 3.55-fold increase (**) in viable cell count ofthe mock transfected control (grey bar) was restricted to a 1.8-foldincrease (*) in the combined anti miR-197 and anti miR-346 (50 nM each)transfected cells (black bar), indicating a significant growth arrest(*p=0.00054). (FIG. 3C) No difference in numbers of non-viable cells (asdetermined by trypan blue stain) was observed between mock transfectedcontrol and anti miR transfected FTC-133 cells, 48 hours aftertransfection (p>0.2).

FIGS. 4A-4E. miR-197- and miR-346-related target gene expression in aset of 14 FTC and 9 FA. (FIG. 4A) RT-PCR analysis of CFLAR and EFEMP2(miR-346-related target genes), ACVR1 and TSPAN3 (miR-197-related targetgenes). (FIG. 4B) Expression of CFLAR, EFEMP2, ACVR1 and TSPAN3 in 4normal thyroid controls. (FIG. 4C) Relative quantitation of expressionof each target gene to that of GAPDH (from A) using spot densitomery.Each bar represents the average normalized band intensity+/−SD of therespective group (FA denoted by grey bars or FTC denoted by black bars)for one target gene (ACVR1, TSPAN3, CFLAR or EFEMP2). Expression levelsof each target gene was significantly lower in FTC compared to FA.*p=0.000014, ** p=0.035, *** p-0.00039, **** p=0.03. ACVR1 proteinexpression in a set of FTC (FIG. 4D) and FA (FIG. 4E) derived from theset of 23 samples used in (FIG. 4A). 2 FTC that show higher ACVR1transcript levels also display increased protein levels, while 3 FTCwith low/absent gene expression show low protein levels.

FIGS. 5A-5B. Effect of miR-197 or miR-346 over-expression on theexpression of their ACVR1 and TSPAN3 determined at 8, 12 and 24 hoursafter transient transfection with pre-miR-197. (FIG. 5A) MultiplexRT-PCR images after transfection (right panel, miR-197 transfection) arecompared to corresponding mock transfection images (left panel, mocktransfection). Maximum reduction in transcript levels, 2.5-fold forACVR1 and 2.1-fold for TSPAN3 occurred at the 12-hour time point (darkgray bars). (FIG. 5B) Expression levels of miR-346 target genes CFLARand EFEMP2 determined at 8, 12 and 24 hours after transfection withpre-miR-346. Maximum reduction in transcript levels for EFEMP2 (1.89fold) was observed at 24 hours after transfection.

FIG. 6 shows the stem-loop sequences (premiR sequences) (SEQ ID NOs.11-14) and the corresponding mature miR sequences (SEQ ID Nos. 15-18)for Homosapian (has) miR-192, miR-197, miR-328 and miR-346.

FIG. 7 Schematic diagram of the PTEN locus and SNPs included in thecurrent analysis.

FIG. 8 Summary of SNP Allele Frequency P-Values for PHTS PatientPopulation Groups Versus Control Population. Allele frequencies amongthree PHTS patient populations (PTEN mutation negative, PTEN mutationpositive and PTEN variation positive) were compared to the controlpopulation for all 30 SNPs using a Pearson χ² test. −log 10 of theP-values were plotted for each comparison and for all SNPs. Note: −log10 P-value 1=P-value 0.1, −log 10 P-value 2=value 0.01, and −log 10P-value 3=P-value 0.001.

FIG. 9 Hemizygous PTEN Deletion Analysis. PTEN copy number was estimatedat exons 2 and 5 using the Livak method for control (n=4), PTENmutation/variation positive (n=4), and PTEN mutation negative samples(n=14) found to be homozygous for all 30 genotyped SNPs, as well as forknown PTEN deletion positive samples (n=2). 2^(−ΔΔCt) values for thecontrol samples ranged from 0.87 to 1.38. PTEN mutation/variationpositive samples (known to have heterozygous PTEN mutations/variations)displayed values between 0.75 and 1.13. PTEN deletion positive sampleshad average 2^(−ΔΔCt) values of 0.67 and 0.53 for exons 2 and 5,respectively. 12 PTEN mutation negative samples had values similar tothe control and PTEN mutation positive samples (0.95 to 1.66). 2 PTENmutation negative samples (1582-02 and 2849-01) displayed 2^(−ΔΔCt)values similar to the PTEN deletion positive samples, ranging from 0.21to 0.72.

FIGS. 10A-10C GOLD plot of pairwise LD between 30 SNPs. D′ values arereported for all three sample groups: FIG. 10A) 94 control samples, FIG.10B) 146 PTEN mutation negative samples, and FIG. 10C) 205 PTENmutation/variation positive samples. The control samples display threedistinct haplotype blocks: block 1 from SNP1 (−30602 G/T) to SNP9(IVS1+2074insA), block 2 from SNP11 (IVS1−13820 A/G) to SNP21 (IVS5−7156A/G), and block 3 from SNP23 (IVS6+457 A/G) to SNP30 (*30414 C/T). SNP10(IVS1−14725delG) and SNP22 (IVS5−2459 T/C) appear to lie near/withinareas of historical recombination. Both the PTEN mutation negative andthe PTEN mutation/variation positive samples display varied LD patternsacross this locus compared to the control population.

FIGS. 11A-11C. Laser capture microdissection (LCM) of the epithelium(FIG. 1 a) and stroma (FIG. 1 b) of squamous cell cancer lesions.Genotyping chromatograms illustrate that in a single sample, LOH/AI(depicted by star) can occur in discordant alleles (D7S1799) orexclusively in one compartment (D14S617 in epithelium; D9S2157 instroma) (FIG. 1 c).

FIG. 12. Correlation between the compartment-specific LOH/AI andclinical characteristics. Each row represents one microsatellite markerwith LOH/AI in the epithelium depicted in red and LOH/AI in stromadepicted by green boxes. The size of the boxes reflects the significanceof LOH/AI and correlation with clinical parameter (small box: p<0.05,and large box: p<0.005). All markers illustrated here hadFPRP_(0.05)<0.5.

FIGS. 13A-13B. Examples of Multiplex PCR Plots Performed for TotalGenome LOH/AI Scanning. In both FIGS. 13A and 13B, the top tracingsrepresent the multiplex genotyping plot for the non-neoplastic normaltissue, and the middle and the bottom tracings are plots for thecorresponding stromal and epithelial compartments, respectively, of thebreast cancer: FIG. 1A, an example of multiplex PCR genotyping forLOH/AI analysis with a primer panel composed of 3 microsatellite markers(D20S851, D4S3243 and D10S212) labeled withtetrachloro-6-carboxy-fluorescein (TET). By comparing the heights of thematched genotypes of normal tissue and tumor stroma or epithelium,LOH/AI was detected in stroma at D20S851 and D10S212, and in epitheliumat D20S851, respectively; FIG. 1B, another example of multiplex PCRgenotyping with a different primer panel composed of 4 markers (ATA5A09,D8S1179, D5S1462 and D3S1763) labeled with6-carboxy-tetramethyl-rhodamine (FAM, in blue) orhexachloro-6-carboxyl-fluorescein (HEX, in green). In this panel, LOH/AIwas detected in stroma at D5S1462 and D3S1763 while no LOH was detectedin epithelium. L; loss of heterozygosity/allelic imbalance, R; retentionof heterozygosity, and H; homozygosity.

FIGS. 14A-14B. Associations between LOH/AI and PresentingClinico-Pathologic Features (CPF). For each chromosome and compartment(labeled to the left of each y-axis) whose LOH/AI frequency (LOH/AIfrequencies are on the y-axis) was found to be related to a CPF(x-axis), the summary statistics of LOH/AI frequency for each level ofthe CPF (I-III for Grade; +, −, +/− for PR and 0, 1, ≧2 for pN) aregiven in a box plot. The characteristics depicted include the mean (linein the middle of each box), the inter-quartile range (height of the box)and outlying observations (additional outside lines above and below eachbox). From the pattern of boxes in each plot, it is evident that each ofthese chromosomes shows a consistently increasing or consistentlydecreasing trend over the levels of the corresponding CPF. For example,for the plot labeled Chr 1 Stromal LOH/AI, the frequencies of LOH/AIstart at an average of slightly under 40% (0.4) with no regional lymphnode metastases (pN0) to 45% at pN1 and rise to an average of 80% forpN2 and above.

DETAILED DESCRIPTION OF THE INVENTION

Provided herein are targets (markers) that can be used for thediagnosis, prognosis and therapy of a variety of cancers. The markersidentified herein include miRNAs and their target genes for detection offollicular thyroid carcinoma (FTC); haplotype blocks for detection ofPTEN Hamartoma Tumor Syndrome (PHTS), and loss ofheterozygosity/alleleic imbalance (LOH/AI) for detection of head andneck squamous cell carcinoma (HNSCC) and breast cancer.

Follicular Thyroid Carcinoma

While the pathogenesis of follicular thyroid carcinoma (FTC) and itsrelation to follicular adenoma (FA) remains unclear, detailedunderstanding of FTC carcinogenesis would facilitate addressing thescientific and clinical challenges given that there are morphologicaland molecular similarities between FTC and the frequently occurring FA.Micro-RNA's (miRNA's) are a new class of small, non-coding RNA'simplicated in development and cancer, and may lend novel clues to FTCgenesis. For the latter process, a deregulated miRNA can orchestrate theaberrant expression of several hundred target genes. Described herein isthe identification of deregulated micro-RNA's in follicular thyroidcancer.

Two high-density expression arrays were used to identify miRNA's andtheir target genes that are differentially expressed between FTC and FA.Validation was done by qRT-PCR. Further, the effect of deregulatedmiRNAs in vitro were functionally characterized using HEK293T, FTC 133and K5 cell lines. In total, 45 primary thyroid samples (23 FTC, 20 FA,4 normal control thyroid) were analyzed.

Two specific miRNA's, miR-197 and miR-346, were significantlyover-expressed in FTC. In vitro over-expression of either miRNA inducedproliferation, while inhibition led to growth arrest. Over-expression ofmiR-197 and miR-346 repressed the expression of their predicted targetgenes in vitro and in vivo.

The observations described herein show that miR-197 and miR-346contribute to FTC carcinogenesis. Both miRNA's and their target genesprovide for novel molecular markers and act as novel targets fortreatment by interference, which could likely normalize the deregulatedprofile of many downstream target genes.

MicroRNAs (miRNAs, miRs) are a class of small, noncoding RNA transcriptsthat are thought to act as key regulators during differentiation anddevelopment (Alvarez-Garcia, I., et al., Development, 132:4653-62(2005)). Each miRNA can influence the expression of several hundreddifferent target genes both at the transcriptional andpost-transcriptional levels (Alvarez-Garcia, I., et al., Development,132:4653-62 (2005); Miska, E. A., Curr. Opin. Genet. Dcv., 15:563-8(2005); Zeng, Y., et al., Proc. Natl. Acad. Sci. U.S.A., 100:9779-84(2003)). While the field of miRNA investigation is still young and manyfunctional aspects need to be elucidated, the availability of highdensity miRNA chip profiling allowed identification of unique signaturesassociated with a variety of human malignancies (Lu, J., et al., Nature,435:834-8 (2005); Chen, C. Z., et al., N. Engl. J. Med., 353:1768-71(2005); Iorio, M. V., et al., Cancer Res., 65:7065-70 (2005); Murakami,Y., et al., Oncogene, 25:2537-45 (2005)). The potential utilization ofmiRNAs as diagnostic and/or prognostic markers has also been described(Chen, C. Z., et al., N. Engl. J. Med., 353:1768-71 (2005); Iorio, M.V., et al., Cancer Res., 65:7065-70 (2005); Murakami, Y., et al.,Oncogene, 25:2537-45 (2005); Calin, G. A., et al., N. Engl. J. Med.,353:1793-801 (2005)). In addition, recent findings indicate that miRNAsshould also be considered as new targets for treatment of diseases(Weiler, J., et al., Gene Ther. 13(6):496-502 (2006)).

Thyroid cancer derived from the follicular epithelial cells account forthe great majority of all thyroid malignancies. Of these, follicularthyroid carcinoma (FTC) accounts for about 10-15%. However, iniodine-deficient areas, the incidence can be twice as high(Surveillance_Research_Branch, Surveillance Epiemiology and End Results,National Cancer Institute 2005; Farahati, J., et al., Thyroid, 14:141-7(2004)). In the clinical setting, FTC poses a special diagnosticchallenge due to the morphological and molecular similarities to thebenign follicular adenoma (FA) (Yeh, M. W., et al., Thyroid, 14:207-15(2004)). Different molecular profiles have been proposed to improvepreoperative diagnosis (Segev, D. L., et al., Acta Cytol., 47:709-22(2003); Cerutti, J. M., et al., J. Clin. Invest., 113:1234-42 (2004);Kebebew, E., et al, Surgery, 138:1102-9; discussion 1109-10 (2005);Umbricht, C. B., et al., Clin. Cancer Res., 10:5762-8 (2004); Weber, F.,et al., J. Clin. Endocrinol. Metab., 90:2512-21 (2005)). However, theaccurate preoperative diagnosis of FTC, especially minimally invasiveFTC, continues to be a challenge. In addition, while thyroid cancer ingeneral has a favorable prognosis, FTC, when diagnosed at an advancedstage is incurable with 10-year survival rates below 40% (Passler, C.,et al., Endocr. Relat., Cancer, 11:131-9 (2004)). Therefore, thechallenge is not only to identify molecular markers for highly accuratediagnostic tests but also to find new targets for treatment of locallyadvanced or metastatic thyroid cancer.

Despite much progress over the recent years, there is a continuedlimited understanding of the molecular and biological relationship ofthe different benign thyroid neoplasias to each other and to thyroidcarcinomas, in particular FTC (Segev, D. L., et al., Surg. Oncol.,12:69-90 (2003); Weber, F., et al., Future Oncology, 1:497-510 (2005)).In contrast to papillary thyroid carcinoma (PTC), the major underlyinggenetic alterations leading to follicular thyroid carcinogenesis remainheterogeneous, even obscure (Segev, D. L., et al., Surg. Oncol.,12:69-90 (2003); Weber, F., et al., Future Oncology, 1:497-510 (2005);Kimura, E. T., et al., Cancer Res., 63:1454-7 (2003)).

Described herein is the investigation of whether the uniformderegulation of a specific set of miRNAs could induce down-regulation ofa cascade of target tumor suppressor genes. It is likely thatidentifying such key molecular differences between FA, which are benignfollicular neoplasias, and FTC, which are malignant follicular thyroidneoplasias, will result in discovering genes and events associated withFTC initiation. Therefore, described herein is the elucidatation of thedifferences in global miRNA expression between FA and FTC which resultedin the dissecting out of deregulated human miRNAs that provides muchneeded improvement in pre-operative diagnosis of FTC versus FA, andtreatment of this cancer.

Accordingly, provided herein are methods of detecting follicular thyroidcarcinoma in an individual. In one embodiment, the method comprisesdetermining expression of one or more microRNAs (miRs) in theindividual, compared to a control. Alternatively, or in addition,expression of one or more target genes of the microRNAs, in theindividual compared to a control can be determined. An increasedexpression of the microRNA, a decreased expression of the target gene ora combination thereof, compared to a control is indicative of follicularthyroid carcinoma in the individual.

In another embodiment, the method comprises distinguishing betweenfollicular thyroid carcinoma and follicular adenoma in an individual. Inthis embodiment, the method comprises determining expression of one ormore microRNAs in the individual, compared to a control. Alternatively,or in addition, expression of one or more target genes of the microRNAsin the individual compared to a control can be determined. An increasedexpression of the microRNAs, a decreased expression of the target genesor a combination thereof, compared to a control is indicative offollicular thyroid carcinoma in the individual. Also, a decreasedexpression of the microRNAs, an increased expression of the target genesor a combination thereof compared to a control, is indicative offollicular adenoma in the individual.

MicroRNAs (miRNAs, miRs) are a class of small, noncoding RNA transcriptsthat are thought to act as key regulators during differentiation anddevelopment (Alvarez-Garcia, I., et al., Development, 132:4653-62(2005)). Each miRNA can influence the expression of several hundreddifferent target genes both at the transcriptional andpost-transcriptional levels (Alvarez-Garcia, I., et al., Development,132:4653-62 (2005); Miska, E. A., Curr. Opin. Genet. Dcv., 15:563-8(2005); Zeng, Y., et al., Proc. Natl. Acad. Sci. USA., 100:9779-84(2003)).

As shown herein, examples of miRs that are overexpressed in FTC includemiR-192, miR-197, miR-328 and miR-346. In a particular embodiment,expression of miR-192, miR-197 and miR-346 are detected in the methods.

Also provided herein are target genes of the miRs (e.g., see Tables 4, 5and 6). Examples of particular target genes can be detected in themethods provided herein include ACVR1, TSPAN3, and EFEMP. In addition,the expressed products of these genes can be detected in the methodsdescribed herein.

In the methods of the invention, a sample can be obtained from theindividual and used in the methods to detect the presence of miRNAand/or the expression of target genes of the miRNAs. Suitable samplesinclude biological fluid (e.g., blood, urine, lymph), cell(s) (e.g.,fetal cells), and/or tissue (e.g., skin, muscle, organ, placenta). Inaddition, nucleic acid and/or protein can be obtained from theindividual or the sample of the individual and used in the methodsdescribed herein. Methods for obtaining a suitable sample or extractingnucleic acid or protein from such samples are described herein and knownto those of skill in the art.

Methods for detecting the expression (presence, level, amount) of miRNAsor expression of a target gene of a miRNA are provided herein and othersuch methods are known to one of skill in the art. Examples of suchmethods include miRNA chip analysis and gel electrophoresis (westernblot).

As described herein, expression of one or more microRNAs in theindividual and/or one or more target genes of the microRNAs in theindividual can be compared to a control. Suitable controls for use inthe methods provided herein are apparent to those of skill in the art.For example, a suitable control can be established by assaying one ormore (e.g., a large sample of) individuals which do not have follicularthyroid carcinoma. Alternatively, a control can be obtained using astatistical model to obtain a control value (standard value; knownstandard). See, for example, models described in Knapp, R. G. and MillerM. C. (1992) Clinical Epidemiology and Biostatistics, William andWilkins, Harual Publishing Co. Malvern, Pa., which is incorporatedherein by reference.

The methods of detecting follicular thyroid carcinoma in an individualand/or distinguishing between follicular thyroid carcinoma andfollicular adenoma in an individual can be performed prior to, or after,surgical intervention (surgery).

The findings herein also provide for methods of inhibiting (partially,completely) proliferation of a (one or more) follicular thyroidcarcinoma cell (e.g., in vitro, in vivo) comprising introducing into thecell one or more agents which inhibit expression or activity of one ormore microRNAs selected from the group consisting of: miR-192, miR-197,miR-346 and a combination thereof. Alternatively, or in addition, one ormore agents which inhibits expression of one or more target genes of amicroRNA selected from the group consisting of: miR-192, miR-197,miR-346 and a combination thereof can be introduced into the cell. Thecells are maintained under conditions in which the one or more agentsinhibits expression or activity of the microRNAs, inhibits expression ofone or more target genes of the microRNAs, or inhibits a combinationthereof, thereby inhibiting proliferation of the follicular thyroidcarcinoma cell.

Methods of identifying an agent that can be used to inhibitproliferation of a follicular thyroid carcinoma cell are also provided.The method comprises contacting one or more microRNAs selected from thegroup consisting of: miR-192, miR-197, miR-346 with an agent to beassessed; contacting one or more target genes of one or more microRNAsselected from the group consisting of: miR-192, miR-197, miR-346 with anagent to be assessed; or contacting a combination thereof. If expressionof the microRNAs is inhibited in the presence of the agent; of ifexpression of the target genes is enhanced in the presence of the agent,or a combination thereof occurs in the presence of the agent, then theagent can be used to inhibit proliferation of a follicular thyroidcarcinoma cell.

Also provided herein are methods of identifying an agent that can beused to treat a follicular thyroid carcinoma. The method comprisescontacting one or more microRNAs selected from the group consisting of:miR-192, miR-197, miR-346 with an agent to be assessed; contacting oneor more target genes of one or more microRNAs selected from the groupconsisting of: miR-192, miR-197, miR-346 with an agent to be assessed;or contacting a combination thereof. If expression of the microRNAs isinhibited in the presence of the agent; of if expression of the targetgenes is enhanced in the presence of the agent, or a combination thereofoccurs in the presence of the agent, then the agent can be used toinhibit proliferation of a follicular thyroid carcinoma cell.

Agents that can be assessed in the methods provided herein include miRNAinhibitors (Ambion; Austin, Tex.). Other examples of such agents includepharmaceutical agents, drugs, chemical compounds, ionic compounds,organic compounds, organic ligands, including cofactors, saccharides,recombinant and synthetic peptides, proteins, peptoids, nucleic acidsequences, including genes, nucleic acid products, and antibodies andantigen binding fragments thereof. Such agents can be individuallyscreened or one or more compound(s) can be tested simultaneously inaccordance with the methods herein. Large combinatorial libraries ofcompounds (e.g., organic compounds, recombinant or synthetic peptides,peptoids, nucleic acids) produced by combinatorial chemical synthesis orother methods can be tested (see e.g., Zuckerman, R. N. et al., J. Med.Chem., 37:2678-2685 (1994) and references cited therein; see also,Ohlmeyer, M. H. J. et al., Proc. Natl. Acad. Sci. USA, 90:10922-10926(1993) and DeWitt, S. H. et al., Proc. Natl. Acad. Sci. USA,90:6909-6913 (1993), relating to tagged compounds; Rutter, W. J. et al.U.S. Pat. No. 5,010,175; Huebner, V. D. et al., U.S. Pat. No. 5,182,366;and Geysen, H. M., U.S. Pat. No. 4,833,092). The teachings of thesereferences are incorporated herein by reference. Where compoundsselected from a combinatorial library carry unique tags, identificationof individual compounds by chromatographic methods is possible. Chemicallibraries, microbial broths and phage display libraries can also betested (screened) in accordance with the methods herein.

The miRs that are overexpressed in FTC and the target genes of thesemiRs (e.g., see Tables 4, 5 and 6) also provide for therapeutic targetsfor treating follicular thyroid carcinoma.

The invention is also directed to kits for detecting follicular thyroidcarcinoma in an individual comprising one or more reagents fordetecting 1) one or more microRNAs selected from the group consistingof: miR-192, miR-197, miR-346 in the individual; 2) one or more targetgenes of one or more microRNAs selected from the group consisting of:miR-192, miR-197, miR-346; 3) one or more polypeptides expressed by thetarget genes or 4) a combination thereof. For example, the kit cancomprise hybridization probes, restriction enzymes (e.g., for RFLPanalysis), allele-specific oligonucleotides, and antibodies that bind tothe polypeptide expressed by the target gene. In a particularembodiment, the kit comprises at least contiguous nucleotide sequencethat is substantially or completely complementary to a region one ormore of the microRNAs. In one embodiment, one or reagents in the kit arelabeled, and thus, the kits can further comprise agents capable ofdetecting the label. The kit can further comprise instructions fordetecting follicular carcinoma using the components of the kit.

PTEN Harmatoma Syndrome

Phosphatase and tensin homolog deleted on chromosome ten (PTEN [MIM601728]) encodes a tumor suppressor gene frequently mutated in bothsporadic and heritable forms of human cancer. Germline mutations areassociated with a number of heritable cancer syndromes referred to asthe PTEN Hamartoma Tumor Syndrome (PHTS) and include Cowden Syndrome (CS[MIM 158350]), Bannayan-Riley-Ravalcaba Syndrome (BRRS [MIM 153480]),Proteus Syndrome (PS [MIM 176920]), and Proteus-like Syndrome (PLS).Germline PTEN mutations have been identified in a significant proportionof patients with PHTS, however, there are still many individuals withclassic diagnostic features for whom mutations have yet to beidentified. To address this, a haplotype-based approach was taken andthe association of specific genomic regions of the PTEN locus with PHTSwas investigated. This locus was found to be characterized by threedistinct haplotype blocks of length 33 kb, 65 kb, and 43 kb,respectively. Comparisons of the haplotype distributions for all threeblocks differed significantly among PHTS patients and controls(P-value=0.0098, <0.0001, and <0.0001, respectively). ‘Rare’ haplotypeblocks and extended haplotypes account for 2- to 3-fold more PHTSchromosomes compared to control chromosomes. PTEN mutation negativepatients are strongly associated with a haplotype block spanning aregion upstream of PTEN and the gene's first intron (P-value=0.0027).Furthermore, allelic combinations contribute to the phenotypiccomplexity of this syndrome. Taken together, these data indicate thatspecific haplotypes and rare alleles underlie the disease etiology inthese sample populations, constitute low-penetrant, modifying loci, and,specifically in the case of PHTS patients where traditional mutationshave yet to be identified, likely harbor pathogenic variant(s) whichhave escaped detection by standard PTEN mutation scanning methodologies.

Phosphatase and tensin homolog deleted on chromosome ten (PTEN [MIM601728]) (also known as mutated in multiple advanced cancers 1 (MMAC1)and tensin-like phosphatase 1 (TEP1)) encodes a tumor suppressorphosphatase that signals down the phosphoinositol-3-kinase (PI3K)/AKTpathway, effecting apoptosis and cell cycle arrest (Eng, C., Hum.Mutat., 22:183-198 (2003); Maehama, T., et al. J. Biol. Chem.,273:13375-13378 (1998); Stambolic V, et al., Cell, 95:29-39 (1998)).Germline PTEN mutations are primarily associated with a number ofapparently clinically distinct heritable cancer syndromes jointlyreferred to as the PTEN Hamartoma Tumor Syndrome (PHTS) (Marsh, D. J.,et al., Hum. Mol. Genet., 8:1461-1472 (1999). These include CowdenSyndrome (CS [MIM 158350]), Bannayan-Riley-Ravalcaba Syndrome (ERRS [MIM153480]), Proteus Syndrome (PS [MIM 176920]), and Proteus-like Syndrome(PLS). All four syndromes are characterized by multiple hamartomatouslesions affecting derivatives of all three germ cell layers. In CS,patients are also at an increased risk of developing breast, thyroid,and endometrial cancer (Eng, C., J. Med. Genet., 37:828-830 (2000);Pilarski, R., et al., J. Med. Genet, 41:323-326 (2004)). To date,germline PTEN mutations have been identified in 85% of patientsdiagnosed with CS and 65% of patients diagnosed with BRRS (Marsh, D. J.,et al., Hum. Mol. Genet., 8:1461-1472 (1999); Zhou, X. P., et al., Am.J. Hum. Genet., 73:404-411 (2003)). Additionally, 20% and 50% ofpatients with PS and PLS, respectively, have also been shown to carryPTEN germline mutations (Smith, J. M., et al., J. Med. Genet.,39:937-940 (2002); Zhou, X., et al., Lancet, 358:210-211 (2001);Loffeld, A., et al., Br. J. Dermatol., 154:1194-1198 (2006)).

Mutation scanning of PTEN has primarily focused on the gene's nine exonsand intron/exon boundaries, which span approximately 103 kilo-basepair(kb) on chromosome sub-band 10q23.3. Germline mutations have beenreported throughout PTEN, with the exception of exon 9, and the majorityof these localize to its phosphatase catalytic core located in exon 5(Eng, C., Hum. Mutat., 22:183-198 (2003); Bonneau, D. et al., Hum.Mutat., 16:109-122 (2000)). More recently, mutations in PTEN's corepromoter region have also been identified and found to be associatedwith CS and increased phosphorylated AKT levels (Zhou, X. P., et al.,Am. J. Hum. Genet., 73:404-411 (2003)). However, despite the significantproportion of patients with known PTEN mutations, there are still manyindividuals with classic PHTS diagnostic features for whom mutationshave yet to be identified. Notably, CS is believed to be linked to thePTEN region, without genetic heterogeneity (Nelen, M. R., et al., Nat.Genet., 13:114-116 (1996)). In BRRS, on the other hand, the extent ofgenetic heterogeneity is unknown. Other mechanisms, such as modifiers ofPTEN or another gene (or genes), which have yet to be identified, may becausal of this syndrome (Marsh, D. J., et al., Hum. Mol. Genet.,8:1461-1472 (1999); Carethers, J. M., et al., Cancer Res., 58:2724-2726(1998)). For individuals with PHTS, particularly those with CS, andwithout identifiable germline mutations, therefore, it is likely thatthe molecular mechanism(s) underlying their disease involves geneticalteration outside of the PTEN coding sequence, possibly involvingelements associated in its trans-regulation, or deregulation, and whichmay lie upstream, downstream, or intronic of PTEN. Identifying themechanism of PTEN dysfunction in these patients is critical and ofsignificant importance to the practice of personalized genetichealthcare.

As described herein, to aid in identifying these genetic alterations, ahaplotype-based approach was used to investigate the association ofspecific genomic regions of the PTEN locus with disease. Through thisapproach, it is demonstrated herein that specific haplotypes, perhapsacting as low-penetrance susceptibility loci, are associated with PHTSin PTEN mutation negative samples. In addition to furthering theunderstanding of the role PTEN has in patients without detectablemutations, specific haplotypes which may act as low-penetrance alleles,or modifying factors, which could influence phenotypic expression in asubset of CR/BRRS patients with known germline PTEN mutations, have alsobeen identified.

Accordingly, the invention provides a method of diagnosing PHTS orsusceptibility to PHTS in an individual comprising detecting thepresence of at least one haplotype block at the individual's PTEN locus(e.g., human chromosome 10).

The PHTS includes, for example, Cowden Syndrome,Bannayan-Riley-Ruvalcaba Syndrome, Proteus Syndrome, Proteus-LikeSyndrome and a combination thereof. In addition, in the methods of theinvention, the individual can be PTEN mutation negative, PTEN mutationpositive or PTEN variation positive.

A haplotype refers to a segment of DNA (e.g., genomic DNA) that ischaracterized by a specific combination of genetic markers (alleles)arranged along the segment (typically along the same chromosome). Amarker refers to a sequence (e.g., genomic sequence) characteristic of aparticular allele (e.g., variant allele). The marker can comprise anyallele such as SNPs, microsatellites, insertions, deletions,substitutions, duplications and translocations. Typically, a haplotypeblock refers to a chromosome region of high linkage disequilibrium andlow haplotype diversity, and are regions of low recombination flanked byrecombination hotspots (e.g., Cardon, L R and Abecasis, G R, Trends inGenetics, 19(3):135-140 (2003)).

In particular embodiments, the haplotype block is selected from thegroup consisting of a block 1 haplotype, a block 2 haplotype, a block 3haplotype and a combination thereof (e.g., extended haplotypes). Thepresence of one or more of the haplotype blocks is indicative of adiagnosis of PHTS or a susceptibility to PHTS in the individual. Block 1haplotypes, block 2 haplotypes, block 3 haplotypes and combinationsthereof (e.g., extended haplotypes) are provided in Tables 9 and 10herein. In the methods of the present invention, the individual can bePTEN mutation negative, PTEN mutation positive or PTEN variationpositive. In a particular embodiment, the individual is PTEN mutationpositive or PTEN variation positive and the haplotype block 1 comprisesthe sequence GACCCTCGI (SEQ ID NO: 19).

Examples of methods for detecting the haplotype blocks are describedherein and other suitable methods are well known to those of skill inthe art. Suitable methods for detecting haplotypes in a sample includesequence analysis, hybridization analysis using a nucleic acid probesuch DNA or RNA (e.g., Northern analysis, Southern analysis, dot blotanalysis), and restriction digestion.

In the methods of the invention, a sample can be obtained from theindividual and used in the methods to detect the presence of thehaplotype blocks. The haplotype block can be detected in any sampleobtained from the individual that comprises the individual's DNA (e.g.,genomic DNA). For example, a haplotype block can be detected in a tissuesample (e.g., skin, muscle, organ, placenta), a cell sample (e.g., fetalcells), a fluid sample (e.g., blood, amniotic fluid, cerebrospinalfluid, urine, lymph) and any combination thereof. Methods of obtainingsuch samples a or extracting nucleic acid from such samples aredescribed herein and known to those of skill in the art.

The detection of the haplotype block in the individual can be comparedto a control. Suitable controls for use in the methods provided hereinare apparent to those of skill in the art. For example, a suitablecontrol can be established by assaying one or more (e.g., a large sampleof) individuals which do not have PTEN Hamartoma Tumor Syndrome.Alternatively, a control can be obtained using a statistical model toobtain a control value (standard value; known standard). See, forexample, models described in Knapp, R. G. and Miller M. C. (1992)Clinical Epidemiology and Biostatistics, William and Wilkins, HarualPublishing Co. Malvern, Pa., which is incorporated herein by reference.

The present invention is also directed to a method of diagnosing PHTS orsusceptibility to PHTS in an individual that is PTEN mutation negativecomprising detecting the presence of at least one haplotype block in thePTEN gene spanning a region upstream of the PTEN gene and the firstintron of the PTEN gene. In a particular embodiment, the haplotype blockin the PTEN gene spans about 33 kb from about position 89,583,605 toabout position 89,616,359 of the genome (e.g., on human chromosome 10).

The haplotype blocks (e.g., see Tables 9 and 10) identified herein alsoprovide for therapeutic targets for treating PTEN Hamartoma TumorSyndrome.

The invention is also directed to kits diagnosing PHTS or susceptibilityto PHTS in an individual comprising one or more reagents for detectingone or more haplotype blocks selected from the group consisting of: ablock 1 haplotype, a block 2 haplotype, a block 3 haplotype and acombination thereof. For example, the kit can comprise hybridizationprobes, restriction enzymes (e.g., for RFLP analysis), allele-specificoligonucleotides, and antibodies. In a particular embodiment, the kitcomprises at least contiguous nucleotide sequence that is substantiallyor completely complementary to a region of one or more of the haplotypeblocks or combinations of haplotype blocks (e.g., a block 1 haplotype, ablock 2 haplotype, a block 3 haplotype, extended haplotype block and acombination thereof). For example, the nucleic acids can comprise atleast one sequence (contiguous sequence) which is complementary(completely, partially) to one or more haplotypes associated with PHTS.In one embodiment, the one or more reagents in the kit are labeled, andthus, the kits can further comprise agents capable of detecting thelabel. The kit can further comprise instructions for detecting PHTSusing the components of the kit.

Head and Neck Squamous Cell Carcinoma (HNSCC)

Carcinogens associated with HNSCC genesis should inflict genomicalterations not only on the epithelium but also the mesenchyme of theaero-digestive tract. Therefore, the apparently non-malignant stromasurrounding the tumor epithelium can acquire genomic alterations andcontribute to cancer initiation and progression.

Described herein is the determination of compartment-specific loci ofloss-of-heterozygosity/allelic imbalance (LOH/AI) and identification ofwhich genomic alterations restricted to the stroma cell populationcontributes to aggressiveness of HNSCC disease.

Tumor epithelium and surrounding stroma were isolated from 122 patientswith oral cavity and oro/hypopharyngeal SCC and subjected to wholegenome LOH/AI analysis using 366 microsatellite markers.

Compartment-specific frequency and distribution of LOH/AI weredetermined and hot-spots of genomic alterations identified.Compartment-specific LOH/AI events were correlated with presentingclinico-pathologic characteristics.

Tumor-associated stroma of HNSCC from smokers were found to have a highdegree of genomic alterations. A clear correlation between tumoraggressiveness could be found for a specific set of 5 loci. Threestroma-specific loci were associated with tumor size (pT) and regionalnodal metastasizes (pN). Further, 2 epithelial-specific LOH/AI hot-spotswere positively correlated with pN status and clinical stage.

Stroma-specific genetic alterations likely to play a role insmoking-related HNSCC genesis. The findings described herein provide notonly novel prognostic or diagnostic biomarkers, but more importantlyidentify new molecular targets for therapeutic and potentiallypreventive intervention. Despite its slowly declining incidence rate(˜4% since 1980) and a modest improvement in 5 year survival (54.4% to59.4% over the last 20 years), squamous cell carcinoma of the head andneck (HNSCC) continues to be a clinical challenge (Forastiere, A., etal., N. Engl. J. Med.; 345:1890-1900 (2001); Ries, LAG H D, et al.,Cancer Statistics Review, 1975-2003: National Cancer Institute (2006)).With a worldwide prevalence of over 1.6 million, it is estimated that in2006, about 30,990 new cases will be diagnosed in the United Statesalone (Ries, LAG H D, et al., Cancer Statistics Review, 1975-2003;National Cancer Institute (2006); American Cancer Society, Oral cancerfacts and figures, Atlanta 2006)). Even with the utilization of allmodern therapeutic options that include surgery, radiation therapy andchemotherapeutic intervention, 50% of all patients will ultimately dieof this disease, with over 7400 this year in the US alone (Ries, LAG. HD, et al., Cancer Statistics Review, 1975-2003: National CancerInstitute (2006); American Cancer Society, Oral cancer facts andfigures, Atlanta 2006)). Especially for patients diagnosed with advancedor relapsed disease, HNSCC is almost uniformly fatal (Ries, LAG H D, etal., Cancer Statistics Review, 1975-2003: National Cancer Institute(2006)).

In order to improve patient management and identify novel compartmentsto target therapy, it is essential to further advance our understandingof this disease at the etiologic level. It is an accepted concept thatHNSCC arises from a successive accumulation of genetic alterations inthe squamous epithelium of the mucosa that will allow one cell to obtaina growth advantage, escape apoptotic signaling, clonally expand andultimately invade and metastasize (Forastiere, A., et al., N Engl. J.Med.; 345:1890-1900 (2001); Perez-Ordonez, B., et al., J Clin Pathol.,59:445-53 (2006); Williams, H. K., Mol Pathol., 53(4):165-72 (2000);Hunter, K. D., et al., Nat Rev Cancer, 5:127-35 (2005)). Several groupshave looked at those genetic alterations and identified mutations in keyregulatory genes including TP53 and p16^(INK4a) as well as geneticinstability in regions such as 3p, 9p, 11q and 17p (Forastiere, A., etal., N Engl. J. Med.; 345:1890-1900 (2001); Perez-Ordonez, B., et al., JClin Pathol., 59:445-53 (2006); Hunter, K. D., et al., Nat Rev Cancer,5:127-35 (2005); Leng, K., et al., J Oral Pathol Med., 35:19-24 (2006);Worsham, M. J., et al., Arch. Otolaryngol. Head Neck Surg., 132:409-15(2006)).

Aggravating the clinical situation is the high rate of recurrent andmultifocal disease in HNSCC (Forastiere, A., et al., N Engl. J. Med.;345:1890-1900 (2001)). This clinical and pathological observation wasfirst addressed by Slaugher et al. and the concept of fieldcancerization was coined (Slaughter, D. P., et al. Cancer, 6: 963-8(1953)). Over the years, it has been related to genetic observations andinterpreted in different ways. The hypotheses include the following:that tumor or their progenitor cells migrate (both intraepithelial orluminal) to the secondary tumor sites, or that tumors occur asindependent events within genetically altered and expanding fields ofpre-neoplastic epithelial cells (Braakhuis, B. J., et al., Cancer Res.,63:1727-30 (2003); (Jang, S. J., et al., Oncogene, 20:2235-42 (2001);van Oijen, M. G., et al., Cancer Epidemiol Biomarkers Prevent, 9:249-56(2000); Braakhuis, B. J., et al., Semin Cancer Biol., 15:113-20 (2005)).However, today, it is known that cancer is not only a disease of thetransformed epithelium but is fundamentally influenced by and dependenton its microenvironment including the stroma in which it develops(Mueller, M. M., Nat. Rev. Cancer, 4:839-49 (2004); McCawley, L. J., etal., Curr. Biol., 11:R25-7 (2001)). The tumor stroma consists offibroblasts, micro-vessels and lymphatic cells and facilitates aphysical and biochemical network that communicates closely with theepithelial cells. Genetic alterations in the stromal cells can lead toaberrant excretion of proteins and misinterpretation of incoming signalsresulting in disruption of the physiologic interplay between epitheliumand stroma (Mueller, M. M., Nat. Rev. Cancer, 4:839-49 (2004); Edlund,M., et al., J. Cell Biochem., 91:686-705 (2004); Weber, F., et al., Br.J. Cancer, 92 (10): 1922-6 (2005)). It has been shown that indeed thestromal fibroblasts of different neoplasias are rich in geneticalterations and can potentially define the tumor phenotype orpotentially induce or sustain the transformation of the pre-neoplasticepithelium in sporadic and BRCA1/2-related breast cancers, prostate andpancreatic cancers, and other solid tumors (McCawley, L. J., et al.,Curr. Biol., 11:R25-7 (2001); Kurose, K., et al., Hum. Mol. Genet.,10(18):1907-13 (2001); Weber, F., et al., Am. J. Hum. Genet. J.,78(6):961-72 (2006); Hill, R., et al., Cell., 123:1001-11 (2005);Condon, M. S., Semin Cancer Biol., 15:132-7 (2005); Ricci, F., et al.,Cancer Biol. Ther., 4:302-307 (2005)). Until now, no study has looked atthe tumor stroma on a comprehensive genomic level in order to addressits role in HNSCC carcinogenesis (Horvath, B., et al., Head Neck,27:585-596 (2005); Rosenthal, E., et al., Mol. Carcinog., 40:116-121(2004)). As described herein a whole genome approach was used,therefore, to determine the extent of genomic alterations in the stromaof HNSCC and whether it correlated with presenting clinico-pathologicfeatures. With this study, described herein is not only the elucidationof the stromal contribution to carcinogenesis and phenotypicdifferentiation of the squamous cell epithelium, but ultimately thefindings point to novel diagnostic and therapeutic options for newcompartments.

Accordingly, the invention is directed to methods of diagnosing head andneck squamous cell carcinomas (HNSCC) or susceptibility to HNSCC in anindividual comprising detecting the presence of a loss ofheterozygosity/allelic imbalance (LOH/AI) at one or more specific loci(markers) in the individual, wherein the presence of the LOH/AI at theone or more specific loci in the individual is indicative of a diagnosisof HNSCC in the individual. In particular embodiments, the HNSCC ispresent in the oral cavity and/or in the pharynx (oro/hypopharygneal) ofthe individual.

Heterozygosity denotes the presence of two alleles which can beindividually discriminated by slight, minor differences in DNA sequencecommonly found at microsatellites, which are segments of DNA composed ofvariable numbers of short repeat units that occur in predictablelocations within the genome but vary in absolute length according of thenumber of repeats. Microsatellite markers can be used to evaluate thetwo different copies or alleles of the human genome. In the normalstate, the two alleles can be distinguished from a each other and aresaid to exist in a state of heterozygosity. When mutations are acquiredwhich typically involve deletion of all or part of an allele, one of thetwo copies is lost from the cell by deletion leading to a loss ofheterozygosity.

“Loss of heterozygosity/alleleic imbalance” typically refers to the lossof a portion of a chromosome in somatic cells (e.g., a deletion,mutation, or loss of an entire chromosome (or a region of thechromosome) from the cell nucleus). Since only one of the two copies ofthe affected chromosomal region originally present in an individual'sgenome will remain in cells which have undergone LOH, all polymorphicmarkers within the region will appear to be homozygous; i.e., thesecells will have lost heterozygosity for these markers. Comparison ofmarker genotypes in a population of cells that are suspected of havingundergone LOH with genotypes of normal tissue from the same individualallows for the identification of LOH, and for mapping the extent of theloss.

In particular embodiments, the LOH/AI is at one or more of the followingloci: D3S3630; D4S2417; D6S305; D18S843; D19S559, in the individual(Table 17).

In the methods of the invention, a sample can be obtained from theindividual and used in the methods to detect the presence of the LOH/AI.The LOH/AI can be detected in any sample obtained from the individualthat comprises the individual's DNA. For example, a LOH/AI can bedetected in a tissue sample (e.g., skin, muscle, organ, placenta), acell sample (e.g., fetal cells), a fluid sample (e.g., blood, amnioticfluid, cerebrospinal fluid, urine, lymph) and any combination thereof.Methods of obtaining such samples a or extracting nucleic acid from suchsamples are described herein and known to those of skill in the art.

Methods of obtaining such samples are well known in the art. In aparticular embodiment, the presence of a LOH/AI at one or more specificloci can be detected in a sample (e.g., tissue, cell, fluid) from thetumor epithelium and/or the surrounding stroma of the tumor epitheliumin the individual. The tumor epithelium and/or surrounding stroma can beobtained using any suitable method known in the art such as lasercapture microdissection (LCM). In addition, the DNA can be extracted andamplified, and the LOH/AI at one or more specific loci can be detected,using any suitable methods known in the art, as described herein. Aswill be apparent to one of skill in the art, methods other than thosedescribed herein can be used.

In particular embodiments, the presence of LOH/AI at one or more of theloci present in stromal cells (e.g., non-malignant stromal cells,malignant stromal cells) surrounding the tumor are detected. The stromalcells can be, for example, fibroblast cells present in the stroma. Inanother embodiment, the presence of LOH/AI at one or more of the locipresent in epithelial cells of the tumor (epithelial tumor cells) aredetected.

The detection of the LOH/AI in the individual can be compared to acontrol. Suitable controls for use in the methods provided herein areapparent to those of skill in the art. For example, a suitable controlcan be established by assaying one or more (e.g., a large sample of)individuals which do not have the LOH/AI at the loci described herein.Alternatively, a control can be obtained using a statistical model toobtain a control value (standard value; known standard). See, forexample, models described in Knapp, R. G. and Miller M. C. (1992)Clinical Epidemiology and Biostatistics, William and Wilkins, HarualPublishing Co. Malvern, Pa., which is incorporated herein by reference.

The methods of the present invention can further comprise determiningtumoral attributes, such as aggressiveness of a tumor or disease, extentof HNSCC tumor invasion (e.g., tumor size (pT status), regional lymphnode status (pN; lymph node involvement; lymph node metastasis)), of anHNSCC tumor present in an individual comprising detecting the presenceof a LOH/AI at one or more specific loci in the genome of theindividual.

In a particular embodiment, the invention is directed to a method ofdetecting an aggressive HNSCC tumor in an individual comprisingdetecting the presence of a LOH/AI at one or more specific loci in thegenome of the individual, wherein the presence of the LOH/AI at the oneor more specific loci in the genome of the individual is indicative ofan aggressive HNSCC tumor in the individual.

The LOH/AI at the one or more specific loci in individuals with HNSCCdescribed herein can also be used as targets for therapeutic and/orpreventive intervention of HNSCC in an individual.

Also provided herein are kits for use in diagnosing HNSCC orsusceptibility to HNSCC in an individual comprising one or more reagentsfor detecting the presence of a LOH/AI at one or more loci selected fromthe group consisting of: D3S3630; D4S2417; D6S305; D18S843; D19S559. Forexample, the kit can comprise hybridization probes, restriction enzymes(e.g., for RFLP analysis), allele-specific oligonucleotides, andantibodies. In a particular embodiment, the kit comprises at leastcontiguous nucleotide sequence that is substantially or completelycomplementary to a region of one or more of the loci comprising theLOH/AI. For example, the nucleic acids can comprise at least onesequence (contiguous sequence) which is complementary (completely,partially) to one or more loci comprising LOH/AI that is associated withHNSCC. In one embodiment, the one or more reagents in the kit arelabeled, and thus, the kits can further comprise agents capable ofdetecting the label. The kit can further comprise instructions fordetecting HNSCC using the components of the kit.

Breast Cancer

Genomic instability within 11 specific genomic regions residing onchromosomes in the tumor stroma of sporadic primary invasive breastcarcinomas correlates with grade and regional lymph node metastases

That genomic alterations occur in both epithelium and stroma of sporadicbreast cancers has been documented by several groups. However, whetherthese microenvironmental alterations relate to clinico-pathologicfeatures is unknown.

Described herein is the analysis of the relationship between stromalgenomic alterations and presenting clinico-pathologic features insporadic breast cancer.

Retrospective analysis of DNA from the epithelium and stroma of 220primary invasive breast carcinomas for global genomic alterationsmanifested by loss of heterozygosity/allelic imbalance with 386microsatellite markers. Regression models and Fisher's exact test wereused to test for associations between loss of heterozygosity/allelicimbalance and clinico-pathologic features.

Association of genetic alterations, in both stroma and epithelium, withpresenting clinico-pathologic features such as tumor grade, expressionstatus of estrogen- and progesterone-receptor and human epidermal growthfactor receptor 2, clinical stage and regional lymph node metastasisstatus.

Significant associations (p=0.0013) between loss ofheterozygosity/allelic imbalance on chromosome 11 in stroma and tumorgrade, on chromosomes 1, 2, 5, 18, 20 and 22 in stroma and regionallymph node metastasis (P=0.0002-0.0016), and on chromosome 14 inepithelium and progesterone receptor expression status (P=0.002) werefound. Specific markers contributing to the LOH/AI on chromosome 11 inthe stroma associating with tumor grade were D11S1999 (p=0.00055) andD11S1986 (p=0.042). Importantly, LOH/AI at various markers in the stromawas significantly associated with pN: ATA42G12 (chrom 1, p=0.00095),D5S1457 (p=0.00095), D5S1501 (p=0.0011), D5S816 (p=0.0008), D18S858(p=0.0026), D20S103 (p=0.0027), D20S851 (p=0.0045), D22S683 (p=0.00033)and D22S1045 (p=0.0013).

The analysis described herein revealed more correlations withclinico-pathologic features and loss of heterozygosity/allelic imbalancein stroma than in epithelium, indicating that stromal genomicalterations help account for clinical diversity and are useful surrogatebiomarkers of prognosis and outcome.

A high degree of variability is observed in both biological behavior andclinical outcome in sporadic breast cancer, and this inter-patientdiversity in breast cancer biology and behavior may confound clinicalmanagement based on “averages”. Breast conserving surgery has become thestandard of care for early stage breast cancer. In a recently publishedstudy, 2929 early stage breast cancer patients were examined for therelative impact of the patient, the surgeon and/or hospital factors onsurgical treatment outcome variation in breast cancer patients. Gort etal found that 91.2% of the total variance was attributable to thepatient level, ie, there is large inter-patient variability (Gort, M.,et al., Breast Cancer Res. Treat., Epub [PMID 17028985] (2006)). Thesedata suggested that inter-patient variation accounts for the high degreeof clinical variability (Gort, M., et al., Breast Cancer Res. Treat.,Epub [PMID 17028985] (2006)). Indeed, the demand for “personalizedmedicine” illustrates the medical community's and public's recognitionof inter-patient variability. It has been recognized for decades thatidentical chemotherapeutic regimens for similar stage and grade patientswith, eg, breast cancer (or virtually any malignancy) responddifferently (Gort, M., et al., Breast Cancer Res. Treat., Epub [PMID17028985] (2006); Weigelt, B., et al., Br. J. Cancer, 93:924-932(2005)). The complexities of genetic alterations in breast cancer mayprovide a primary basis for these consequent (ie, secondary)clinico-pathologic features (CPFs) an idea supported by prior positivecorrelations between certain breast cancer genotype and phenotype(Simpson, P. T., et al., J Pathol., 205: 248-254 (2005)). For example,well-differentiated (grade I) breast cancers show a low number ofgenetic alterations with highly recurrent losses of 16q, while poorlydifferentiated (grade III) cancers show complex genetic changescontaining DNA losses as well as DNA amplifications (Simpson, P. T., etal., J Pathol., 205: 248-254 (2005)). However, many previous studiesfocused only on restricted regions of the genome harboring knowntumor-associated genes, such as TP53, or were limited to small series ofpatients. High throughput genome-wide scanning for genetic alterationscan now be performed on larger series of clinical samples to discovergenotypic-phenotypic correlations unbiased by prior work. Moreover,virtually all previous studies exploring these somaticgenotype-phenotype correlations fail to separately analyze malignantepithelium and reactive host elements. Tumor microenvironment,incorporating both invasive epithelium and reactive host elements,dynamically determines cancer behavior (Bissell, M. J., et al., J. CellSci. Suppl., 8: 327-343 (1987); Shekhar, M. P., et al., Cancer Res.,61:1320-1326 (2001)). The contribution of cancer-associated stromal cellgenetic changes to this interaction have been variously ascribed toepigenetic changes (DNA methylation) (Allinen, M., et al., Cancer Cell.,6: 17-32 (2004); Hu, M., et al., Nat. Genet., 37: 899-905 (2005)), ormutation, as has been shown for tumor-associated stroma from breast,colon, bladder and ovarian cancers (Moinfar, F., et al., Cancer Res.,60:2562-2566 (2000); Kurose, K., et al., Hum. Mol. Genet., 10:1907-1913(2001); Wernert, N., et al., Anticancer Res., 21:2259-2264 (2001);Kurose, K., et al., Nat. Genet., 32:355-357 (2002); Fukino, K., et al.,Cancer Res., 64:7231-7236 (2004); Tuhkanen, H., et al., Int. J. Cancer,109:247-252 (2004)). Previous work with breast cancer revealed thattumor associated stroma may contain a higher density of geneticalterations than the malignant epithelium itself (Fukino, K., et al.,Cancer Res., 64:7231-7236 (2004)). In the current study of sporadicbreast carcinomas, whether stromal cell genomic alterationssignificantly alter tumor behavior, as reflected in clinicopathologicfeatures at the time of diagnosis, was investigated.

Accordingly, provided herein are methods of diagnosing breast cancer orsusceptibility to breast cancer in an individual comprising detectingthe presence of a loss of heterozygosity/allelic imbalance (LOH/AI) atone or more specific loci (markers) in the individual, wherein thepresence of the LOH/AI at the one or more specific loci in theindividual is indicative of a diagnosis of breast cancer in theindividual.

In one embodiment, the invention is directed to methods of diagnosingbreast cancer or susceptibility to breast cancer in an individualcomprising detecting the presence of a LOH/AI at one or more lociselected from the group consisting of: D11S1999, D11S1986, ATA42G12,D5S1457, D5S1501, D5S816, D18S858, D20S103, D20S851, D22S683, D22S1045in the individual, wherein the presence of the LOH/AI at the one or moreof eleven specific loci in the individual is indicative of a diagnosisof breast cancer in the individual. In one embodiment, one or more ofthe loci are present in the stroma (e.g., non-malignant stroma)surrounding a tumor epithelium and/or the epithelium of the tumor.

In the methods of the invention, a sample can be obtained from theindividual and used in the methods to detect the presence of the LOH/AI.The LOH/AI can be detected in any sample obtained from the individualthat comprises the individual's DNA. For example, a LOH/AI can bedetected in a tissue sample (e.g., skin, muscle, organ, placenta), acell sample (e.g., fetal cells), a fluid sample (e.g., blood, amnioticfluid, cerebrospinal fluid, urine, lymph) and any combination thereof.Methods of obtaining such samples a or extracting nucleic acid from suchsamples are described herein and known to those of skill in the art.

Methods of obtaining such samples are well known in the art. In aparticular embodiment, the presence of a LOH/AI at one or more specificloci can be detected in a sample (e.g., tissue, cell, fluid) from thetumor epithelium and/or the surrounding stroma of the tumor epitheliumin the individual. The tumor epithelium and/or surrounding stroma can beobtained using any suitable method known in the art such as lasercapture microdissection (LCM). In addition, the DNA can be extracted andamplified, and the LOH/AI at one or more specific loci can be detected,using any suitable methods known in the art, as described herein. Aswill be apparent to one of skill in the art, methods other than thosedescribed herein can be used.

In particular embodiments, the presence of LOH/AI at one or more of theloci present in stromal cells (e.g., non-malignant stromal cells,malignant stromal cells) surrounding the tumor are detected. The stromalcells can be, for example, fibroblast cells present in the stroma. Inanother embodiment, the presence of LOH/AI at one or more of the locipresent in epithelial cells of the tumor (epithelial tumor cells) aredetected.

The detection of the LOH/AI in the individual can be compared to acontrol. Suitable controls for use in the methods provided herein areapparent to those of skill in the art. For example, a suitable controlcan be established by assaying one or more (e.g., a large sample of)individuals which do not have the LOH/AI at the loci described herein.Alternatively, a control can be obtained using a statistical model toobtain a control value (standard value; known standard). See, forexample, models described in Knapp, R. G. and Miller M. C. (1992)Clinical Epidemiology and Biostatistics, William and Wilkins, HarualPublishing Co. Malvern, Pa., which is incorporated herein by reference.

The methods of the present invention can further comprise determiningbreast cancer tumoral attributes, such as aggressiveness of the tumor ordisease, extent of breast tumor invasion (e.g., tumor size (pT status;tumor grade), regional lymph node status (pN; lymph node involvement;lymph node metastasis)), of a breast cancer tumor present in anindividual comprising detecting the presence of a LOH/AI at one or morespecific loci in the genome of the individual.

In a particular embodiment, the invention is directed to a method ofdetecting an aggressive breast cancer tumor in an individual comprisingdetecting the presence of a LOH/AI at one or more specific loci in theindividual, wherein the presence of the LOH/AI at the one or morespecific loci in the individual is indicative of an aggressive breastcancer tumor in the individual.

The LOH/AI at the one or more specific loci in individuals with breastcancer described herein can also be used as targets for therapeuticand/or preventive intervention of breast cancer in an individual.

Also provided herein are kits for use in diagnosing breast cancer orsusceptibility to breast cancer in an individual comprising one or moreregents for detecting the presence of a LOH/AI at one or more lociselected from the group consisting of: D11S1999, D11S1986, ATA42G12,D5S1457, D5S1501, D5S816, D18S858, D20S103, D20S851, D22S683, D22S1045.For example, the kit can comprise hybridization probes, restrictionenzymes (e.g., for RFLP analysis), allele-specific oligonucleotides, andantibodies. In a particular embodiment, the kit comprises at leastcontiguous nucleotide sequence that is substantially or completelycomplementary to a region of one or more of the loci comprising theLOH/AI. For example, the nucleic acids can comprise at least onesequence (contiguous sequence) which is complementary (completely,partially) to one or more loci comprising LOH/AI that is associated withbreast cancer. In one embodiment, the one or more reagents in the kitare labeled, and thus, the kits can further comprise agents capable ofdetecting the label. The kit can further comprise instructions fordetecting breast cancer using the components of the kit.

As used herein the term “individual” includes animals such as mammals,as well as other animals, vertebrate and invertebrate (e.g., birds,fish, reptiles, insects (e.g., Drosophila species), mollusks (e.g.,Aplysia). Preferably, the animal is a mammal. The terms “mammal” and“mammalian”, as used herein, refer to any vertebrate animal, includingmonotremes, marsupials and placental, that suckle their young and eithergive birth to living young (eutharian or placental mammals) or areegg-laying (metatharian or nonplacental mammals). Examples of mammalianspecies include primates (e.g., humans, monkeys, chimpanzees), rodents(e.g., rats, mice, guinea pigs) and ruminents (e.g., cows, pigs,horses).

In addition, as used herein a cell can be a germ cell or somatic cell.Suitable cells can be of, for example, mammalian (e.g., human) origin.

Identification of the markers of the particular cancers described herein(e.g., miRNAs and their target genes for follicular thyroid carcinoma;haplotype blocks for PTEN Hamartoma Tumor Syndrome, and loss ofheterozygosity/alleleic imbalance for head and neck squamous cellcarcinoma and breast cancer) provide for methods of detecting recurrenceof the cancer in an individual that is in remission, or has been treatedfor the cancer comprising detecting the markers in the individual.

In addition, the markers provide for methods of screening anasymptomatic individual for the particular cancer comprising detectingthe marker in the asymptomatic individual.

Also encompassed by the present invention are methods of monitoring atreatment regimen for cancer in an individual comprising monitoring themarker(s) in the individual undergoing or completing a particulartreatment regimen.

The present invention also provides for methods of monitoring anindividual at risk for developing the particular cancer by assaying forthe presence of the marker(s) in the individual at regular intervals(e.g., once every 6 months; once a year; once every two years).

Example 1 MicroRNAs Deregulated in Follicular Thyroid Carcinoma

Materials and Methods

Tissue Specimens

In total, 47 thyroid samples (23 FTC, 20 FA and 4 normal controlthyroid) were analyzed in this study (Table 2 for detailed histologies).No oncocytic or hypercellular adenomas were analyzed in this study. Aset of 8 FA and 12 FTC were used for the miRNA-chip array and a setcomprising 12 FTC and 12 FA was analyzed on the GeneChip array. 6 FTCand 6 FA overlapped in these 2 studies. Additional validation of thedifferentially expressed miRNAs was performed in an independent set of 9follicular neoplasias (5 FTC and 4 FA) and 4 normal control thyroid, notused on the miRNA-chip. Gene expression validation was done in a set of14 FTC and 9 FA by quantitative RT-PCR. The study, which utilizedanonymized unlinked samples, was approved by the participatingInstitutional Review Boards for Human Subjects' Protection.

miRNA-Chip Expression Analysis

The miR chip analysis followed the design and protocols as describedpreviously by Liu et al., except that the human & mouse microRNA 11Kversion 2 chip was used (Liu, C. G., et al., Proc. Natl. Acad. Sci.U.S.A., 101:9740-4 (2004)). In brief, following biotin end-labeling, thesmall RNAs were hybridized on a custom microRNA array chip that contains460 mature miRNA probes (235 Homo sapiens, 222 Mus musculus and 3Arabidopsis manual). The 235 human miRNA are derived from a total of 319(73.7%) unique, mature miRNAs known today. For each miRNA, 40-mer 5′amine modified C6 oligos were printed in quadruplicate on AmershamCodeLink activated slides (Amersham, Piscataway, N.J.). Quantificationof biotin-containing transcripts was achieved after chip washing,processing and incubation with streptavidin-Alexa647 using the Axon4000B scanner and GENEPIX Pro 6.0 software package (Molecular Devices,Sunnyvale, Calif.). A detailed description of sequence selection, chipconstruction and array protocols can be found on EMBL-EBI, Array Express# E-TABM-68.

MicroArray Expression Analysis

Total RNA extraction was performed under standard protocol using theTRIzol Reagent (Invitrogen, Carlsbad, Calif.) and purified with theRNeasy Kit (Qiagen, Valencia, Calif.). The sample preparation,hybridization and analysis were performed as described previously indetail (Weber, F., et al., J. Clin. Endocrinol. Metab., 90:2512-21(2005); Aldred, M. A., et Clin. Oncol., 22:3531-9 (2004); Auer, H., etal., Nat. Genet., 35:292-3 (2003)). Chip data can be obtained fromEMBL-EBI, Array Express # E-MEXP-97.

miRNA and Gene Expression Validation

The mirVana miRNA isolation kit was used for isolation and enrichment ofsmall RNA fractions (Ambion, Austin, Tex.). MicroRNA expression analysiswas done for miR197, miR-328 and miR-346 by quantitative RT-PCR,according to the manufacturer's protocols (Ambion, Austin, Tex.).Optimized primers for the reverse transcription (RT) and polymerasechain reaction (PCR) are commercially available (Ambion, Austin, Tex.).

Endpoint PCR was done with HotStar Taq Polymerase (Qiagen, Valencia,Calif.) and primers as followed: ACVR1 5′-TTCCTCACTGAGCATCAACG (SEQ IDNO. 1) and 5′-TAATGAGGCCAACCTCCAAG (SEQ ID NO. 2); TSPAN35′-AGCCCTGCTTTTCATCATTG (SEQ ID NO. 3) and 5′-TTCTGAATGCTGCGATCAAC (SEQID NO. 4); EFEMP2 5′-GCCCAAACCTGTGTCAACTT (SEQ ID NO. 5) and5′-ATGAAGGCTGCTCTCGACAT (SEQ ID NO. 6); CFLAR 5′-TTTCTTTGCCTCCATCTTGG(SEQ ID NO. 7) and 5′-GAAGCTCACAAGGGTCTTGC (SEQ ID NO. 8), GAPDH5′-GGGCTGCTTTTAACTCTGGTAA (SEQ ID NO. 9) and 5′-ATGGGTGGAATCATATTGGAAC(SEQ ID NO. 10).

Cell Lines and Culture Conditions

The HEK293T, human embryonic kidney cells, 2 human follicular thyroidcancer cell lines (FTC133 and K5) and 1 human papillary thyroid cancercell lines (NPA87) were cultured in DMEM supplemented with 10% fetalbovine serum (FBS), and 100 units/ml penicillin and streptomycin (LifeTechnologies, Invitrogen). For cell growth assay equal numbers (90,000)of cells were plated in 12-well plates. After 8, 12, 24 and 48 hours,the medium was removed and the cells were washed and harvested. Aftertrypsinization, viable cells (excluding trypan blue) were counted.

Transient Over-Expression of miRNAs

Precursor miRNAs (prec-miR-197 and prec-miR-346) (Ambion, Austin, Tex.)were transiently transfected into HEK293T cells with the siPORT NeoFxtransfection reagent (Ambion, Austin, Tex.). For mock transfectionconditions, prec-miR was substituted with random oligonucleotides atequal concentration. Optimal transfection efficiency was empiricallydetermined at 3 μl siPORT NeoFx, 10 nM small RNA for 90,000 cells. Allexperiments were done in triplicate.

Suppression of Endogenous miRNA Function

Commercially available anti-miR™ miRNA inhibitors (Ambion, Austin, Tex.)directed against each of the mature sequences of miR-197 and miR-346were transfected into 2 human thyroid carcinoma cell lines (FTC133 andK5) as well as NPA87 (human papillary thyroid carcinoma) cell line, tostudy the effect on growth potential. 20 to 80 nM of anti-miRoligonucleotides (Ambion, Austin, Tex.) were transfected with the siPORTNeoFX transfection agent (3 μl) into the respective cells (90,000cells/well of a 12-well plate).

Protein Isolation and Western Blot

Protein was isolated from tumor samples using RIPA buffer (50 mM Tris pH8.0, 150 mM NaCl, 1% Triton and 0.1% SDS) containing proteases andsubsequently sonication. Protein extracts (15 μg) were separated on a10% SDS-PAGE gel and electrophoretically transferred ontonitrocellulose. After blocking for non-specific binding, blots were thenincubated with either ACVR1 (Abgent; San Diego, Calif.) or Actin (Sigma;Saint Louis, Miss.) primary antibody (1:1000 in 3% BSA). Followingincubation with an anti-rabbit secondary antibody (1:2500 dilution in 5%milk; Promega; Madison, Wis.) the protein bands were visualized usingenhanced chemiluminescence as described by the manufacturer (AmershamPharmacia Corp; Piscataway, N.J.).

Statistical Methods

For the miRNA-chip data, spots flagged as poor quality during imageanalysis were excluded from analysis. The average intensity overquadruplicate spots for each miRNA was computed and a log base 2transformation was then applied to the expression values. Amedian-centering array normalization procedure was then performed toallow for comparison across arrays. The primary interest was comparingmiRNA expression between FA and FTC patient samples. Since array sampleswere hybridized at two different times, the possibility of a batcheffect was accounted for by using a 2-way ANOVA with batch as a blockvariable. The 2 hybridization sets included both FA and FTC samples,with 3 FA and 5 FTC in the first and 5 FA and 7 FTC in the second set. Anominal significance level of 0.001 was employed in all statisticalcomparisons. BRB ArrayTools Version 3.3 (National Cancer Institute,Rockville, Md.) was used for all analyses. GeneChip HG-U133A raw datawere analyzed with the DNA-Chip Analyzer Software (dChip) developed byLi and Wong (www.dchip.org) as described by us previously in detail(Weber, F., et al., J. Clin. Endocrinol. Metab., 90:2512-21 (2005)). Alinear diagonal discriminant analysis was used for class prediction inthe gene expression data. The performance of the predictor was testedusing leave-one-out cross-validation method based on 2000 randompermutations. A 2-tailed Student's T-test for independent samples,assuming equal variance, was used to determine difference between meangene expressions in the validation analysis and cell growth assay. Foranalysis between groups, Fisher 2-tailed exact test was used.

Results

Based on a high-density custom miRNA chip 4 miRs were identified,miR-192, miR-197, miR-328 and miR-346 (p=0.00009, 0.00063, 0.00021 and0.000496, respectively), all of which are over-expressed in FTC comparedto FA (1.34, 1.82, 1.48 and 1.39 fold) (Table 1 and Table 3). Two miRNAs(miR-192 and miR-197) have previously been experimentally validated inhuman (i.e., are truly human miR expressed in human tissues), whilemiR-328 and miR-346 are only predicted human homologues; however, theirexpression in human tissue has now been shown (FIGS. 1A-1C)(Lagos-Quintana, M., et al., Rna, 9:175-9 (2003)).

Validation of miR Over-Expression

In an independent set of 9 follicular thyroid neoplasias (5 FTC and 4FA) and 4 normal control thyroids, the differential expression of themature miR-197 (over-expressed in FTC vs. FA by 2.00-fold, p=0.0044) andmiR-346 (1.37-fold expressed in FTC over FA, p=0.049) were validatedusing quantitative RT-PCR (FIGS. 1A-1C). miR-192 was restricted to insilica analyses because specific reverse transcription and PCR primersfor miR-192 could not be designed and tissue availability did not allowfor analysis by Northern Blot hybridization. However, for miR-328, eventhough the average expression was higher in FTCs compared to FAs, thisdifference did not meet statistical significance in this validation set(p>0.08; data not shown), and was not pursued further.

Functional Effect of Identified miRNAs

The functional consequences of miRNA over-expression were determined bytransient transfection of 2 of the identified and most robustlyvalidated miRNAs (miR-197 and miR-346) in a human non-neoplastic cellline (HEK293T). First, transfection efficiency was confirmed bydetecting overexpression of miR-197 and miR-346 above endogenous levels(FIG. 2A). At 12 and 24 hours after miR-197 or miR-346 transfection,significantly induced cell proliferation was noted with approximately1.5-fold more viable cells than before transfection (p=0.003-0.049; seeFIG. 2B and legend). For both miR-197 and miR-346, expressional levelswere seen to peak at 12 hours post-transfection and begin to return tobasal levels by 24 hours (FIG. 2A). The non-viable cell populationincreased by factors of 1.7 to 2.28-fold and thus mirrors the increasein the viable cell count observed in miR-197 and miR-346 transfectedcells (FIG. 2C).

Suppression of Endogenous miRNA Function and Effect on Growth Potential

Commercially available miRNA inhibitors (Ambion, Austin, Tex.) were usedto suppress the functional effect of endogeneous miRNA-197 and miR-346.FTC-133 cells under control conditions resulted in a 2.31-fold increasein cell number within 48 hr (absolute cell count at 48 hours vs. 0hours, FIG. 3A). In contrast, transfection of anti-miR-197 and/oranti-miR-346 into FTC-133 cells resulted in a 2-fold growth suppression,(i.e., a 1.11 to 1.5-fold increase in cell number instead of the control2.31-fold was noted during the same time period (48 hours vs. 0 hours)).The effect of this miRNA inhibition on FTC-133 cell proliferation wassignificant (p=0.0128, 0.0016 and 0.0026, respectively, FIG. 3A). Asimilar effect was seen in a second human FTC cell line (K5) (FIG. 3B)while neither inhibitor showed any effect in the NPA-87 cell line whichlacks endogenous miRNA-197 and miRNA-346 over-expression (data notshown). The number of non-viable cells did not differ between anti-miR™oligonucleotide and control conditions (FIG. 3C).

In Silico Analysis of Predicted miRNA Target Gene Expression

The MicroCosm web resource (Version 2.0) maintained by the SangerInstitute was utilized to predict potential miRNA target sequences andre-interrogated the data from previously published gene expression array[HG-U133A, 12 FTC and 12 FA] for these target genes (Weber, F., et al.,J. Clin. Endocrinol. Metab., 90:2512-21 (2005)). For miR-197, 57 of the496 represented target genes showed significant under-expression in FTCscompared to FA when using a cut off value of −1.5-fold and a maximump-value of 0.05 (Table 4). Using the same criteria, 24 out of the 278target genes for miR-346 and 51 out of 379 target genes predicted formiR-192 were significantly under-expressed in FTCs compared to FAs(Tables 5 and 6).

To ensure specificity of the findings in the context of FTC, thisanalysis was repeated using the predicted target genes for miR-221,miR-222 and miR-146a, which are specific for papillary thyroidcarcinogenesis (He, H., et al., Proc. Natl. Acad. Sci. USA.,102:19075-80 (2005)). These analyses revealed that the PTC-miR's are notdifferentially regulated between FTC and FA. Between 418 and 566 targetgenes were present on the HG-U133A chip, but of those, only 20(miR-146a, 4.8%) to 29 (miR-222, 5.1%) genes were significantlyunder-expressed in FTC. This is significantly less than what wasobserved for the FTC-specific miR-192 (13.5%, p<0.000004), miR-346(8.6%, p<0.018) and miR-197 (11.5%, p<0.00011).

Validation of Predicted Target Genes

In order to verify that in silco predicted miRNA targets genes can beregulated by the respective miRNA in vitro, 2 out of 57 miR-197 targets(ACVR1, TSPAN3), and 2 out of 24 miR-346 target genes (EFEMP2, CFLAR),were selected for proof of principle (Tables 4, 5). The 2 target genes(ACVR1 and TSPAN3) for miR-197 that were significantly under-expressedin FTC compared to FA (1.9- and 1.5-fold, p=0.00039 and p=0.03) and tonormal thyroid control (FIGS. 4A-4E) were successfully validated. ForACVR1, differences in gene transcript expression were reflected byprotein levels as well (FIGS. 4 D, 4E). Similarly, the 2 miR-346 targetgenes EFEMP2 and CFLAR, were under-expressed by 2.2-fold (p=0.035) and1.9-fold (p=0.000014) in FTCs compared to FAs (FIGS. 4A-4E).

In the HEK293T cell model, over-expression of miR-197 leads to reducedmRNA levels of ACVR1 and TSPAN3 at 12 hours (down 2.5- and 2.0-fold,respectively) and 24 hours (down 1.35- and 1.5-fold, respectively) (FIG.5A). Interestingly, over-expression of miR-346 resulted in a continuousreduction of EFEMP2 mRNA levels at both 12 hours (down 1.2-fold) and 24hours (down 1.89-fold) (FIG. 5B). In contrast, over-expression ofmiR-346 did not significantly influence the transcript levels of CFLARin our HEK293T model (FIG. 5B). Neither miRNA had any effect on the genetranscription of non-target genes (e.g., ACVR1, TSPAN3 for miR-346 andCFLAR, EFEMP2 for miR-197).

In addition, the performance of these 3 validated miRNA target genes(ACVR1, TSPAN3 and EFEMP2) were evaluated as a molecular classifier todistinguish FTC and FA. Based on the expression of ACVR1, TSPAN3 andEFEMP2, using established linear discriminant analysis and employingleave-one-out cross-validation, 88% of class labels (e.g., FTC or FA)were correctly predicted based on re-mined expression array data (Weber,F., et al., J. Clin. Endocrinol. Metab., 90:2512-21 (2005); Radmacher,M. D., et al., J. Comput. Biol., 9:505-11 (2002)). This was furtherconfirmed by using the second sample set, analyzed by RT-PCR. Here thisACVR1-TSPAN3-EFEMP2 profile allowed accurate identification of 87% ofthe samples as benign or malignant, providing a sensitivity of 85.7% (12out of 14) and specificity of 88.9% (8 out of 9) to identify FTC.

Discussion

Over the last few years, numerous molecular alterations have beendescribed that are likely to participate in the development of benignand malignant neoplasias derived from thyroid follicular epithelialcells (Cerutti, J. M., et al., J. Clin. Invest., 113:1234-42 (2004);Umbricht, C. B., et al., Clin. Cancer Res., 10:5762-8 (2004); Weber, F.,et al., J. Clin. Endocrinol. Metab., 90:2512-21 (2005); Segev, et al.,Surg. Oncol., 12:69-90 (2003); Aldred, M. A., et al., J. Clin. Oncol.,22:3531-9 (2004); Aldred, M. A., et al., Oncogene, 22:3412-6 (2003);Sarquis, M. S., et al., J. Clin. Endocrinol. Metab. 91:262-9 (2006);Weber, F., et al., J. Clin. Endocrinol. Metab. 90:1149-55 (2005); Fagin,J. A., Endocrinology, 143:2025-8 (2002); Kraiem. Z., et al., Thyroid,10:1061-9 (2000)). However, the evolution of events causing malignanttransformation is still limited. In this study using a high-densitymiRNA chip platform, only 4 human small RNAs (miRNA), miR-192 (1103.1),miR-197 (1p13.3), miR-328 (16q22.1) and miR-346 (10q23.2) that areover-expressed in FTC compared to FA were identified. None of thesemiRNAs have previously been associated with thyroid neoplasia and appearto be specific for follicular thyroid carcinomas. It is interesting tonote that only a few miRNAs are deregulated between FTC and FA. Otherstudies, comparing cancer to their matching normal tissue, identified asmany as 30 differentially regulated miRNAs (Chen, C. Z., et al., N.Engl. J. Med., 353:1768-71 (2005); Iorio, M. V., et al., Cancer Res.,65:7065-70 (2005); Murakami, Y., et al., Oncogene, 25:2537-45 (2005);He, H., et al., Proc. Natl. Acad. Sci. U.S.A., 102:19075-80 (2005)). Themajority of these miRNA expressional differences occurred in the rangebetween 1.2- and 2-fold, similar to what we observed in our study(Iorio, M. V., et al., Cancer Res., 65:7065-70 (2005); Murakami, Y., etal., Oncogene, 25:2537-45 (2005)). Based on these observations,especially those made in PTC (He, H., et al., Proc. Natl. Acad. Sci.U.S.A., 102:19075-80 (2005)), one might hypothesize that thederegulation of several miRNA's—not identified in this study—occurequally in benign and malignant follicular neoplasia.

Functional Effect of miR-197 and miR-346

Over-expression of the most robustly validated miRNAs (miR-197 andmiR-346) induced marked proliferation in vitro. As proof of principle,the functional link between miR-197 and miR-346 and the transcriptionalsuppression of 3 target genes was validated. First, EFEMP2 (or fibulin4) is involved in stabilization and organization of ECM structures(Argraves, W. S., et al., EMBO Rep., 4:1127-31 (2003)). There isevidence that EFEMP2 harbors tumor-suppressor functions, which wereshown herein to be inhibited by miR-346 deregulation (Argraves, W. S.,et al., EMBO Rep., 4:1127-31 (2003); Gallagher, W. M., et al., FEBSLett., 489:59-66 (2001)). Second, as a functional consequence ofderegulated miR-197 in FTC, ACVR1 as well as tetraspanin 3 (TSPAN3)becomes under-expressed. Activin A as well as TGF-B1 are ligands for theactivin A receptors type 1 (ACVR1) and have been shown to be potentgrowth inhibitors in various human cells, including thyroid epithelium(Schulte, K. M., et al., Thyroid, 11:3-14 (2001)). While no functionaldata exist on TSPAN3, there are such data for CD63, another member ofthe tetraspan superfamily with highest homology to TSPAN3 (Boucheix, C.,et al., Expert Rev. Mol. Med., 2001:1-17 (2001)). Expression levels havebeen shown to be inversely correlated with the metastatic potential inmelanoma (Boucheix, C., et al., Expert Rev. Mol. Med., 2001:1-17 (2001);Schulte, K. M., et al., Horm. Metab. Res., 32:390-400 (2000)). Finally,the findings provided herein show the limitations of in silco analysiswhen identifying miRNA target genes. For one (CFLAR) out of the 4 genestested in this study, a functional link between the miRNA and thepotential target gene could not be established in vitro despite insilico evidence.

Implications of Deregulated miRNAs for the Accurate Pre-OperativeDiagnosis of FTC

The over-expression of a small set of miRNA's with subsequent cascadingdown regulation of target tumor suppressor genes, represents a powerfulmechanism where a small but significant (1.2- to 2-fold range)over-expression can lead to larger downstream perturbations thatinactivate numerous genes potentially participating in FTC-genesis.These miRNAs and their target genes, therefore, likely provide novelmolecular markers to accurately differentiate malignant (FTC) and benignthyroid neoplasia (FA). Based on the set of differentially expressedmiRNAs (miR-192, miR-197, miR-328 and miR-346) in our miRNA-Chipexperiment, class labels (FTC versus FA) in 74% of all cases could becorrectly predicted. However, the usefulness of miRNAs for diagnosticpurposes should be considered since in follicular thyroid neoplasias,the diagnosis must rely on material obtained from fine needle aspirationbiopsies and it is our observation that needle wash out material doesnot provide enough of the small RNA fraction for reproducible analysis(unpublished observation). Therefore, the target genes of these miRNA'slikely provide for better diagnostic markers. Using the common approachof diagonal linear discriminant analysis and leave-one-out-crossvalidation method (Weber, F., et al., J. Clin. Endocrinol. Metab.,90:2512-21 (2005); Radmacher, M. D., et al., J. Comput. Biol., 9:505-11(2002)), the miRNA target gene classifier (ACVR1, TSPAN3 and EFEMP2)described herein achieved an accuracy of over 87% to differentiatebetween FTC and FA in 2 independent sample sets (see Results). While themolecular markers presented here perform similarly well as otherproposed models based on gene expression profiling such as reported byCerutti et al. (e.g., 83% accuracy) or Umbricht et al. (e.g., 77%accuracy), it does not perform superiorly to our previously identified3-gene signature (96.7% accuracy) (Cerutti, J. M., et al., J. Clin.Invest., 113:1234-42 (2004); Umbricht, C. B., et al., Clin. Cancer Res.,10:5762-8 (2004); Weber, F., et al., J. Clin. Endocrinol. Metab.,90:2512-21 (2005)). Nonetheless, all minimally invasive FTCs (03E077,03E191 and 03E192) were correctly identified as a malignancy using themiRNA target gene classifier (ACVR1, TSPAN3 and EFEMP2). Considering theadvancement over the last years to identify and validate such molecularmarkers, the currently unanswered question will need to be addressed.That is, if indeed there is an adenoma-carcinoma sequence in follicularthyroid cancer, what will be the treatment of choice for those patientsdiagnosed with FA preoperatively?

Suppression of Endogenous miRNA Expression—Clinical Implications

In the human thyroid cancer cell line models described herein, theintroduction of synthetic chemically modified anti-miRNA™oligonucleotides directed against miR-197 or miR-346 induced asignificant growth arrest. This phenomenon was observed both in FTC-133and K5 FTC cells, while the papillary thyroid cancer cell line (NPA87),lacking deregulation of these miRNA's, was not affected. Recently it hasbeen discussed and tested that interference with miRNA function opensnovel opportunities for therapeutic intervention (Weiler, J., et al.,Gene Ther., 13(6):496-502 (2006)); Grunweller, A., et al., Curr. Med.Chem., 12:3143-61 (2005); Poy, M. N., et al., Nature, 432:226-30 (2004);Krutzfeldt, J., et al., Nature, 438:685-9 (2005)). The study describedherein provides in vitro evidence for the feasibility of this approachfor FTC, something that clearly will need further in vivo validation.However, it is likely that the interference with the deregulated miRNAprofile in FTC might allow re-activation of suppressed target genes andultimately affect an array of downstream targets to reverse themalignant phenotype or at least cause growth arrest. In addition, thefindings provided herein indicate that the interference with specificmiRNA(s) is not only cancer-type specific but also could besub-histology-specific in a given type of cancer, in this case, specificfor FTC. In contrast, shown herein is that miR-221 and miR-222, whichare implicated in PTC carcinogenesis, do not play in role in follicularneoplasia development (He, H., et al., Proc. Natl. Acad. Sci. U.S.A.,102:19075-80 (2005)).

In conclusion, the study described herein shows that a small set ofdifferentially regulated miRNAs are specifically deregulated infollicular thyroid cancer and likely participate in the transformationfrom benign to malignant neoplasia. These small RNAs and their targetgenes point to new targets to improve preoperative diagnosis offollicular nodule, and even therapy for a disease that continues tochallenge us in the clinical setting.

TABLE 1 miRNA's differentially expressed between FTC and FA microRNAFA^(a) FTC^(a) fold difference p-value hsa-miR-197^(b) 848.3 1545.7−1.82 0.0004969 hsa-miR-328^(b) 666.2 990.4 −1.49 0.0000991hsa-miR-346^(b) 620.4 862.2 −1.39 0.0006331 hsa-miR-192 552.6 741.5−1.34 0.0002103 ^(a)Values indicate average normalized expression forthe respective microRNA for 12 FTC or for 8 FA analyzed on the OSU-CCCmicroRNA Chip version 2.0. ^(b)miRNAs further analyzed by qRT-PCR in anindependent set of 9 follicular neoplasias comprising 5 FTC and 4 FA

TABLE 2 Histopathological description of 23 follicular thyroid carcinomaused for analysis Sample ID Histopathology sex/age size 02E187^(b,c)FTC, oxyphilic type, widely invasive na 2.2 03E077^(b,c) FTC, minimallyinvasive, oxyphillic type f/48 2.5 133^(c) FTC, oxyphilic type, widelyinvasive f/83 na 03E139^(b,c) FTC, oxyphilic type, widely invasive f/613.0 177^(b,c) FTC, well differentiated, widely invasive f/78 na FC5^(c)FTC, well differentiated, widely invasive na na 1928T^(c) FTC, insulartype na na 52^(b,c) FTC, recurrence m/40 na FC9^(c) FTC, welldifferentiated, widely invasive na na A^(c) FTC, well differentiated,widely invasive m/68 3.8 03E192^(a,b,c) FTC. minimally invasive f/25 na22^(a,b,c) FTC, well differentiated, widely invasive f/67 2.5 04E341^(a)FTC, oxyphilic type, widely invasive f/63 2.0 04E342^(a) FTC, insulartype f/75 1.5 95^(a,b,c) FTC, recurrence f/69 na 05E222^(a) FTC,moderately invasive f/65 1.2 03E193^(a,b) FTC, oxyphilic type, minimallyinvasive f/82 5.0 05E094^(a) FTC, well differentiated, widely invasivem/49 4.4 03E191^(a,b,c) FTC, minimally invasive f/62 2.4 02E187^(a) FTC,oxyphilic type, widely invasive na 2.2 05E159^(a) FTC, moderatelyinvasive f/73 5.2 408^(a,b) FTC, oxyphilic type, widely invasive f/712.0 03E041^(b) FTC, oxyphilic type, metastasized f/72 na f = female, m =male, na—not available. ^(a)tumors analyzed on the miRNA Chip,^(b)tumors analyzed on the HG-U133A GeneChip, ^(c)tumors used forvalidadtion. Size indicates maximal diameter in cm of the tumor.Minimally invasive, tumor invasion through the entire thickness of thetumor capsule; moderately invasive, tumor with angioinvasion, with orwithout tumor invasion through the entire thickness of the tumorcapsule; widely invasive, broad area(s) of transeapsular invasion.

TABLE 3 Normalized Log-Transformed miRNA Expression for SignificantmiRNAs hsa- ID Type miR-197 hsa-miR-328 hsa-miR-346 hsa-miR-192 04E428FA 8.749 8.660 8.860 8.594 02E167 FA 10.517 9.921 9.422 9.348 02E226 FA9.068 8.814 9.042 8.936 03E180 FA 9.402 8.799 8.883 8.666 02E191 FA10.257 9.399 9.610 9.429 478T FA 10.118 9.895 9.415 9.450 03E080 FA10.118 9.895 9.415 9.450 05E165 FA 9.598 9.656 9.571 9.007 mean 9.7289.380 9.277 9.110 2^(mean) 848.3 666.2 620.5 552.6 03E192 FTC 10.5329.774 9.320 9.628 22 FTC 10.395 9.903 10.154 9.360 04E341 FTC 10.2049.477 9.505 9.074 04E342 FTC 9.634 9.257 9.630 8.847 95 FTC 10.562 9.6949.370 9.237 05E222 FTC 10.663 10.265 9.870 9.811 03E193 FTC 11.05710.303 9.733 9.744 05E094 FTC 10.415 9.810 9.707 9.662 03E191 FTC 11.25310.247 9.973 9.774 02E187 FTC 11.804 10.041 9.850 9.985 408 FTC 10.55010.677 9.930 9.858 05E159 FTC 10.059 9.974 9.980 9.433 mean 10.594 9.9529.752 9.534 2^((mean)) 1545.7 990.4 862.2 741.6

TABLE 4 Predicted miR-197 target genes differentially expressed betweenFTC and FA Gene Expression - HG-U133A Target Fold Prediction^(b) GeneFA^(a) FTC^(a) Change P value Score^(b) P value CHIC2 152.7 88.2 −1.730.00010 16.49 0.00018 CPNE6 78.7 32.4 −2.43 0.00010 16.15 0.00591TSPN3^(c) 642.2 265.6 −2.42 0.00033 15.90 0.00118 HNF4A 21.6 7.7 −2.800.00220 15.53 0.00095 WDR6 341.4 204.1 −1.67 0.00052 17.87 0.00494 ABCC359.0 9.4 −6.28 0.02287 15.99 0.00031 VDP 225.4 80.4 −2.80 0.00018 14.730.00068 ZNF302 182.5 89.0 −2.05 0.00005 16.46 0.03331 FBXW7 74.3 33.0−2.25 0.00018 14.70 0.00011 ACVRl^(c) 401.5 201.5 −1.99 0.00004 16.570.03303 PIPOX 40.1 24.6 −1.63 0.00180 16.65 0.00206 RAD51 37.8 23.3−1.62 0.00007 17.04 0.02393 PEX13 56.8 27.9 −2.04 0.00047 15.47 0.00656TAF4B 176.4 94.5 −1.87 0.00001 15.97 0.04413 RXRB 37.6 17.0 −2.220.01302 17.45 0.01921 HNRPD 819.5 528.0 −1.55 0.00022 15.77 0.00163MMP23A 37.9 18.4 −2.06 0.00188 14.70 0.00144 CPSF1 90.3 44.3 −2.040.00387 15.76 0.00501 DPH2L1 64.0 31.9 −2.01 0.00011 15.17 0.01221 RAB2828.0 14.9 −1.88 0.00144 16.38 0.03498 DCBLD2 46.2 26.6 −1.74 0.0093415.35 0.00018 AGR2 353.4 25.1 −14.08 0.02230 15.69 0.00682 THRAP5 33.721.6 −1.56 0.03617 17.06 0.00035 HMGN1 1583.2 1022.4 −1.55 0.00014 15.430.00353 CLIC1 885.3 560.7 −1.58 0.00091 16.60 0.03277 PRKD2 133.4 84.5−1.58 0.00015 14.94 0.00311 NP_057452.1 127.6 77.2 −1.65 0.00352 15.080.00087 KNS2 113.8 69.2 −1.65 0.00271 15.44 0.00391 TSPYL1 985.5 530.9−1.86 0.00015 14.61 0.00821 CREBL1 39.4 25.2 −1.56 0.00908 18.72 0.00903ALMS1 100.4 64.6 −1.55 0.00211 17.24 0.02302 RBM4 447.1 284.4 −1.570.00079 16.44 0.03364 LRP4 167.6 42.4 −3.95 0.03356 14.82 0.00220 DPYSL361.6 15.4 −4.00 0.04569 14.77 0.00187 FUS 450.8 300.0 −1.50 0.0010314.83 0.00010 HPN 196.1 109.7 −1.79 0.01887 15.92 0.00830 FOXO3A 89.259.1 −1.51 0.00284 16.39 0.00858 EHD2 44.1 26.1 −1.69 0.01074 15.270.00274 IER3 284.7 137.4 −2.07 0.01939 15.68 0.02243 SNX1 598.5 339.3−1.76 0.00260 15.81 0.04812 GOLGB1 254.8 165.2 −1.54 0.00479 15.660.00317 ZNF I75 75.0 46.1 −1.63 0.00225 15.95 0.04444 IGF2AS 52.3 22.9−2.28 0.04991 15.97 0.04085 PHF20 143.1 94.8 −1.51 0.00306 14.96 0.00102CES1 154.7 75.3 −2.05 0.04815 16.15 0.03985 GORS2 418.2 272.2 −1.540.00133 15.40 0.00949 CDK10 142.3 89.8 −1.58 0.00952 14.72 0.00066 RFX125.8 16.0 −1.62 0.01539 14.93 0.00222 GALT 160.6 95.9 −1.67 0.0050115.83 0.04761 CYLD 153.4 96.3 −1.59 0.00750 15.34 0.01079 KLF10 323.8178.5 −1.81 0.00860 14.69 0.00913 UMPS 193.6 128.2 −1.51 0.00311 14.990.00587 ZNF208 22.8 14.6 −1.57 0.01316 15.99 0.04344 NEK4 100.5 66.2−1.52 0.00852 15.38 0.01313 ICB1 70.9 47.1 −1.51 0.02337 14.92 0.00351IL1R1 218.6 135.8 −1.61 0.04093 14.74 0.01041 PRKAR2A 179.0 116.1 −1.540.00735 14.58 0.01974 ^(a)Model Based Expression Index, dChip software;^(b)Target Sequence prediction score and p-value based on the MicroCosmversion 2.0 Web Resource (Sanger Institute); ^(c)Genes selected for invitro analyses

TABLE 5 Predicted miR-346 target genes differentially expressed betweenFTC and FA Gene Expression - HG-U133A Target Prediction^(b) Gene FA^(a)FTC^(a) Fold Change P value Score^(b) P value EFEMP2^(c) 338.9 120.2−2.82 0.00000 17.49 0.02557 DHRS6 699.3 347.4 −2.01 0.00009 16.970.00660 GALT 160.6 95.9 −1.67 0.00501 18.10 0.00008 SERHL 280.4 165.4−1.70 0.00027 16.48 0.00060 ENTPD1 262.7 30.7 −8.56 0.00767 15.700.00068 FNTB 102.7 62.6 −1.64 0.00481 17.51 0.00087 GGTLA1 154.6 61.9−2.50 0.00118 16.91 0.03356 GJA12 79.1 31.0 −2.55 0.00012 15.38 0.00893C21 orf18 72.1 40.8 −1.77 0.00116 15.18 0.00053 TSTA 3 164.0 89.6 −1.830.00122 16.52 0.00829 CFLAR^(c) 295.9 194.5 −1.52 0.00261 16.60 0.00182SSH3 52.2 27.4 −1.90 0.00849 16.08 0.00268 CRELD1 246.9 163.7 −1.510.00247 17.31 0.00635 INRC5 75.7 45.5 −1.66 0.00472 15.72 0.00281 NR2F681.3 40.0 −2.03 0.01936 15.84 0.00701 CD3Z 35.2 18.2 −1.93 0.00761 16.570.04112 TERF1 126.0 65.9 −1.91 0.00096 15.34 0.03542 RXRB 37.6 17.0−2.22 0.01302 15.20 0.00310 DGCR2 411.8 225.4 −1.83 0.00552 15.400.00687 IL11RA 215.0 130.9 −1.64 0.00948 15.41 0.00009 P1B5PA 649.2374.5 −1.73 0.00944 15.61 0.00317 MAPK8IP1 61.6 36.0 −1.71 0.00351 15.290.02222 THRAP5 33.7 21.6 −1.56 0.03617 17.20 0.02810 RFX1 25.8 16.0−1.62 0.01539 15.56 0.02250 ^(a)Model Based Expression Index, dChipsoftware; ^(b)Target Sequence prediction score and p-value based on theMicroCosm version 2.0 Web Resource (Sanger Institute); ^(c)Genesselected for in vitro analyses

TABLE 6 Predicted miR-192 target genes differentially expressed betweenFTC and FA Gene Expression - HG-U133A Target Fold Prediction^(b) GeneFA^(a) FTC^(a) Change P value Score^(b) P value CLIC1 885.3 560.7 −1.580.00091 15.43 0.04719 PANX1 83.8 49.3 −1.70 0.00548 16.53 0.00718 SPARC784.8 368.5 −2.13 0.00429 14.31 0.00086 ODC1 520.2 341.1 −1.52 0.0109615.54 0.00045 DDOST 1257.9 722.2 −1.74 0.00106 14.53 0.00053 ABCG2 96.154.5 −1.76 0.04190 15.83 0.03715 EGR1 1097.7 322.2 −3.41 0.00842 15.060.01068 TFG 503.3 292.5 −1.72 0.00111 14.92 0.00029 DDX3X 148.7 73.4−2.03 0.00241 15.42 0.00957 WDR44 66.9 43.8 −1.53 0.00734 14.45 0.00330E2F5 59.2 32.7 −1.81 0.00902 14.70 0.00883 LOXL2 87.9 51.6 −1.70 0.0012215.00 0.00660 NP_065789.1 331.5 166.6 −1.99 0.00009 14.28 0.00257 XPA160.5 84.5 −1.90 0.00160 16.78 0.00531 BARD1 47.4 25.2 −1.88 0.0066816.29 0.02798 RBL2 103.0 63.8 −1.61 0.00495 15.52 0.04475 RAB2 756.5490.3 −1.54 0.00346 19.08 0.00011 CUL3 534.1 338.7 −1.58 0.00008 14.270.00870 MAP3K1 28.1 16.1 −1.74 0.01657 14.69 0.04935 PERP 263.1 132.3−1.99 0.00795 14.25 0.00894 TP5M1 229.4 128.4 −1.79 0.00174 15.080.00845 ABCA8 231.2 84.1 −2.75 0.01449 15.35 0.04950 ATP10D 97.5 59.1−1.65 0.00891 15.12 0.04831 MSN 1045.7 662.2 −1.58 0.00132 15.07 0.01406SPFH2 217.1 134.6 −1.61 0.00212 15.25 0.00463 ABCC3 59.0 9.4 −6.280.02287 15.63 0.00204 GRIA1 24.3 16.2 −1.50 0.03581 15.89 0.03688 ATXN7111.6 65.7 −1.70 0.00034 15.44 0.04787 TRA1 4024.7 2291.2 −1.76 0.0001614.41 0.00100 RRM1 114.5 75.1 −1.53 0.00215 15.13 0.00533 ENTPD3 95.654.5 −1.75 0.02257 14.52 0.01229 B3GALT3 53.5 33.3 −1.61 0.02072 17.950.01003 BRD3 319.9 209.2 −1.53 0.02485 17.46 0.00106 ALC M 1061.4 490.1−2.17 0.00012 14.58 0.00070 STX7 225.1 148.7 −1.51 0.00015 14.59 0.02461CD164 1605.8 1037.5 −1.55 0.00378 14.83 0.02120 PTP4A3 80.3 46.1 −1.740.02645 17.05 0.01755 IGSF4 1497.6 856.5 −1.75 0.00702 16.26 0.02847 C21orf18 72.1 40.8 −1.77 0.00116 14.83 0.03812 PDE2A 111.8 58.4 −1.920.00111 14.36 0.01510 AKAP9 241.5 152.5 −1.58 0.00099 15.81 0.03756ENOSF1 200.2 133.1 −1.50 0.01241 14.76 0.00003 RANBP3 46.6 27.1 −1.720.00693 14.54 0.00134 GOLGA6 57.0 37.1 −1.54 0.02277 15.12 0.01369RABGAP1 440.0 219.2 −2.01 0.00009 14.28 0.00344 NP_00101242 1827.9 746.0−2.45 0.00149 15.35 0.04964 N_001111.2 204.0 100.0 −2.04 0.00001 17.370.01431 NP_006324.1 304.9 168.8 −1.81 0.00292 16.77 0.02081 SEMA4D 95.356.3 −1.69 0.00107 15.25 0.00174 PIK3R4 208.1 103.3 −2.02 0.00026 14.520.00674 ^(a)MODEL BASED EXPRESSION INDEX, DCHIP SOFTWARE; ^(b)TARGETSEQUENCE PREDICTION SCORE AND P-VALUE BASED ON THE MICROCOSM VERSION 2.0WEB RESOURCE (SANGER INSTITUTE)

Example 2 Detecting PTEN Hamartoma Tumor Syndrome (PHTS) Based onHaplotype Association

Materials and Methods

Study Subjects

A total of 447 unrelated subjects were included in the current analysis.94 white control subjects, 148 white PHTS patients without detectablegermline PTEN mutations (i.e., PTEN mutation negative patients), and 205white PHTS patients with previously identified germline PTENmutations/variations (i.e. PTEN mutation/variation positive patients).DNA for control subjects (Utah residents with ancestry from northern andwestern Europe) was acquired from the Coriell Institute for MedicalResearch (Camden, N.J.). All PHTS samples were enrolled by referral fromcenters located throughout the United States, Canada and Europe.Informed consent was acquired for all referred subjects in accordancewith procedures approved by the Human Subjects Protection Committees ofeach respective institution.

Among the PTEN mutation negative patients, 94 were classic CS, 10patients were classic BRRS, 4 patients exhibited features of both CS andBRRS (termed CS-BRRS overlap), and 39 patients exhibited a CS-likephenotype (i.e., patients with some features of CS, but not meetingoperational diagnostic criteria). One PTEN mutation negative patientcould not be classified.

The cohort of PTEN mutation/variation positive patients included 103mutation positive samples (i.e. samples with pathogenic heterozygousmissense or nonsense mutations) and 102 variation positive samples. Thislatter group consists primarily of individuals with identified variantsof unknown significance (VUS) located in the PTEN core promoter regionor within potential splice donor/acceptor sites. Among the PTEN mutationpositive samples, 34 were classic CS, 18 were classic BRRS, 10 exhibitedfeatures of CS-BRRS overlap, and 40 were classified as CS-like. One PTENmutation positive patient could not be classified. The PTEN variationpositive samples included 39 patients with classic CS, 2 samples withclassic BRRS, 6 samples with both CS and BRRS features, and 52 CS-likesamples. Three PTEN variation positive patients could not be classified.

All patients classified as CS in the current study meet operationalcriteria established by the International Cowden Consortium and curatedby the National Comprehensive Cancer Network (Pilarski, R., et al., J.Med. Genet., 41:323-326 (2004)).

SNP Genotyping

SNPs spanning the PTEN locus and located approximately one every 5 kbwere selected from the dbSNP database for validation and estimation ofminor allele frequency in a 10-sample screening set consisting of 5white control subjects and 5 white patient samples. 24 screened SNPswere found to have a minor allele frequency≧0.10, and met our criteriafor inclusion in this study. To achieve a uniformly spaced SNP map, 6additional SNPs with a minor allele frequency≧0.10 were identified byDNA resequencing in our screening set. All 30 SNPs were genotyped in our447 sample cohort. Polymerase chain reactions (PCRs) included 12.5 μlHotStarTaq Master Mix (Qiagen, Valencia, Calif.), 10 mM forward primer,10 mM reverse primer, and 20 ng of template DNA and used the followingthermal cycling conditions: 95° C. for 15 min, 34 cycles of 95° C. for30 s, 50-58° C. for 45 s, and 72° C. for 1 min, followed by a 72° C.final extension for 10 min. 29 SNPs were genotyped using eitherrestriction fragment length polymorphism (RFLP), SNaPshot (Applied.Biosystems, Foster City, Calif.), or fragment analysis. SNaPshot andfragment analysis products were electorphoresed using an ABI 3730 DNAAnalyzer (Applied Biosystems, Foster City, Calif.) and analyzed usingGeneMapper v3.5 software (Applied Biosystems, Foster City, Calif.).rs12573787 was genotyped by direct DNA resequencing. Primer sequencesand genotyping methodologies are provided in Table 12.

Hemizygous PTEN Deletion Analysis

Real-time quantitative PCR was used to investigate potentialmicro-deletions in both control (n=4) and PTEN mutation negative patientsamples (n=14) where homozygosity was observed for all 30 SNPs. 15 PTENmutation/variation positive samples were also homozygous for SNPsassayed in this region, however, by virtue of their heterozygousmutations/variations, these samples are assumed to carry two copies ofthe PTEN allele. Copy number determinations were made for our targetgene, PTEN exons 2 and 5, and a control reference gene, GAPDH exon 7. 4homozygous control samples and 4 homozygous PTEN mutation/variationpositive samples were used as negative controls. Additionally, 2 samplespreviously determined to have PTEN deletions (one spanning the entirePTEN locus, the other spanning both the PTEN and BMPR1A genes) wereassayed as positive controls. PCR efficiencies for each amplicon weredetermined by standard curve analysis using serial dilutions of genomicDNA from a control sample (100 ng, 50 ng, 25 ng, and 12.5 ng,respectively). The calculated PCR efficiencies for these ampliconsranged from 76-81%.

Determination of gene copy number was assayed using 12.5 μl iQ SYBRGreen Supermix (Bio-Rad Laboratories, Hercules, Calif.), 10 mM forwardprimer, 10 mM reverse primer, and 20 ng of template DNA. Thermal cyclingconditions comprised of 95° C. for 3 min and 40 cycles at 95° C. for 30s followed by 58° C. for 30 s and 72° C. for 30 s using an ABI 7700Sequence Detection System (Applied Biosystems, Foster City, Calif.).Target and reference genes were assayed in triplicate for each sampleand subject to meltcurve analysis in order to determine ampliconspecificity. The relative quantification of gene copy number for bothPTEN amplicons was determined using the comparative delta Ct method(2^(−ΔΔCt)) as described by Livak et al, (Livak, K. J., et al., Methods,25:402-408 (2001)).

Linkage Disequilibrium and Haplotype Analysis

Following assessment of Hardy-Weinberg equilibrium at each polymorphiclocus, pairwise LD coefficients (Lewontin's D′) were estimated using theLDmax software program and visualized using the GOLD graphical interface(Abecasis, G. R., et al., Bioinformatics, 16:182-183 (2000)). D′ wascalculated and plotted separately for each sample population (controlsubjects, PTEN mutation negative patients, and PTEN mutation/variationpositive patients). LD blocks were determined using data from thecontrol population and the dynamic programming algorithms implemented inthe HapBlock software program (Empirical LD method, D′>0.90 for strongLD) (Zhang, K., et al., Proc. Natl. Acad. Sci. USA., 99:7335-7339(2002); Gabriel, S. B., et al., Science, 296:2225-2229 (2002)).Following block partitioning, haplotype phase was reconstructed for eachblock and all genotyped samples using the SNPHap software program, basedon pair-wise LD measurements and the expectation-maximization (EM)algorithm, and the PHASE v2.1 software program, based on a Bayesianapproach (Clayton, D., et al., Genet. Epidemiol, 27:415-428 (2004);Stephens, M., et al., Am. J. Hum. Genet., 68:978-989 (2001)).Additionally, haplotype phase was reconstructed for the extended 30 SNPhaplotype for all samples.

Statistical Analysis

Allele and genotype frequencies were computed for each SNP. P-values forHardy-Weinberg equilibrium (HWE) were obtained and Bonferroni adjustmentwas applied to control the overall type-I error rate at 0.05. Eachpatient group (sharing the same mutation status) was compared to thecontrols in their allele and genotype distributions for each SNP.Following haplotype reconstruction, haplotype from PHASE were selectedfor comparisons. For each block and the extended block, a number oftests were performed. First, haplotype frequencies in all phenotypegroups with distinct mutation statuses were compared using a Pearson χ²test, where rare haplotypes (expected frequency less than 5 for anygroup) were pooled together to make the chi-square approximationaccurate as determined by the criterion of Cochran (Cochran, W.,Biometrics, 10:417-451 (1954)). Bonferroni adjustment were applied tothe four overall tests using the significance level of 0.05/4 (0.0125)for each test. Each pair of groups was then compared using a Pearson χ²test with the same criterion of pooling rare haplotypes.

If the result of the overall test is statistically significant(P-value<0.0125), the subsequent pairwise tests provide more specificcomparisons between groups. The first χ² test controls the overalltype-1 error rate but further adjustment were made for multiple testsbetween pairs of groups by using 0.05/6 (0.0083) as the significantlevel for each such test.

Following this, groups with different clinical features were compared interms of the haplotype frequencies using the same approach of an overallPearson χ² tests and subsequent comparisons of each group (one at atime) with the controls, pooling rare haplotypes in each test asdescribed above. The same set of tests was performed for the controlsand the subset of patients classified as mutation positive or mutationnegative. Similarly to the first group of test, we use 0.0125 as thesignificance level for each overall test to adjust for the total numberof blocks (4, including 3 haplotype blocks and the extended block), and0.0125 as the significance level for each subsequent pairwise comparisonto adjust for the number of groups being compared with the control groupin turn.

Results

SNP Analysis and Identification of Hemizygous Deletions

As described herein, an informative marker set comprised of 30relatively evenly spaced SNPs (one SNP every 5.6 kb, with a minor allelefrequency greater than 10%) across a 163 kb region spanning the entirePTEN locus and including 30 kb of flanking sequence was developed (FIG.7 and Table 7). The majority of identified SNPs are intronic (18/30); 11are outside of the gene (7 upstream and 4 downstream), and one SNP islocated in PTEN's 5′ untranslated region (UTR). These include 19transitions, 5 transversions, and 6 insertion/deletion polymorphisms.Table 8 shows the allele frequencies for all 30 polymorphisms genotypedin the control and PHTS patient populations. No significant departuresfrom HWE were observed. FIG. 8 summarizes the −log 10 P-values fromcomparisons of allele frequencies among PTEN mutation negative, PTENmutation positive, and PTEN variation positive groups versus the controlpopulation. Overall, results from 13/90 comparisons (14%) weresignificant at the 0.05 level. Specifically, the allele frequency ofSNP2 differed significantly among PTEN mutation positive samples andcontrol samples (P-value=0.0083). More strikingly, the allelefrequencies of SNPs 10, 12, 14, 19, 24, 25 and 27 were all significantlydifferent from the control population among the PTEN variation positivegroup (P-values<0.01). Additionally, SNPs 16 and 17 both achievedstatistical significance for this same comparison (P-values=0.0127 and0.0123, respectively).

33/447 samples (7.4%) were found to be homozygous for all 30 SNPs in ourpanel, including: 4/94 control samples (4.3%), 14/148 PTEN mutationnegative samples (9.5%), and 15/205 PTEN mutation/variation positivesamples (7.3%). Because heterozygosity has previously been identified inthe PTEN mutation/variation positive samples, PTEN copy numberdeterminations were only made for the control and PTEN mutation negativesamples. Previously we reported that 2^(−ΔΔCt) values close to 1indicates the presence of two PTEN alleles, while values close to 0.5are indicative of hemizygous PTEN deletions (Zhou, X. P., et al., Am. J.Hum. Genet., 73:404-411 (2003)). As shown in FIG. 9, the control sampleswere found to have average 2^(−ΔΔCt) values of 1.09±0.14 for PTEN exon 2and 1.06±0.20 for PTEN exon 5, confirming that these samples retain twocopies of PTEN. Similarly, a subset of PTEN mutation/variation positivesamples had average 2^(−ΔΔCt) values of 0.94±0.14 for PTEN exon 2 and0.97±0.12 for PTEN exon 5. Two samples known to harbor hemizygousgermline deletions spanning the entire PTEN locus displayed averagevalues of 0.67 and 0.53 for the two PTEN amplicons, respectively. 12homozygous PTEN mutation negative samples exhibited 2^(−ΔΔCt) valuessimilar to those observed in the control and PTEN mutation/variationpositive samples (1.14-1.66 for PTEN exon 2 and 0.95-1.51 for PTEN exon5). Two samples, 1582-02 (0.46 for PTEN exon 2 and 0.21 for PTEN exon 5)and 2849-01 (0.72 for PTEN exon 2 and 0.57 for PTEN exon 5) had2^(−ΔΔCt) values that were consistent with hemizygous deletions. Becauseof their hemizygous status at this locus, both 1582-02 and 2849-01 wereexcluded from the subsequent LD and haplotype analyses.

Linkage Disequilibrium Along the PTEN Locus

Three distinct haplotype blocks characterized by strong LD in thecontrol population were found (FIG. 10A). Block 1 spans SNP1 (−30602G/T) to SNP9 (IVS1+2074insA) (33 kb), block 2 spans SNP11 (IVS1−13820A/G) to SNP21 (IVS5−7156 A/G) (65 kb), and block 3 spans SNP23 (IVS6+457A/G) to SNP30 (*30414 C/T) (43 kb). Adjacent to each partitioned block,LD decays. SNP10 (IVS1−14725delG) displayed average D′ values of 0.75and 0.85 with blocks 1 and 2, respectively, and could not be assigned toeither block. Similarly, SNP22 (IVS5−2459 TIC) had an average D′<0.90and was not in strong LD with either adjacent block, suggesting thatboth SNPs lie in/near putative recombination hot-spots. The PTENhaplotype structure in two PHTS patient populations (146 unrelated PTENmutation negative and 205 unrelated PTEN mutation/variation positivePHTS patient samples) are shown in FIGS. 10B and 10C, respectively.Similar to the control population, significant LD was observed for theentire region. However, compared to controls, the overall LD patternsobserved in the PHTS patient samples appear to be distinct. LD in thesesamples suggests less recombination of the adjacent blocks and thepresence of extended haplotypes across this locus.

Haplotype Association Analysis at the PTEN Locus

Having identified three regions of strong LD flanked by two apparentrecombination hot-spots, the haplotypes contained within each LD blockwere investigated next. Haplotype phase was reconstructed using both theSNPHap and PHASE software programs. The two algorithms performedsimilarly, agreement was reached for 98.8% of the reconstructedhaplotype blocks and for 96.5% of the reconstructed chromosomes (i.e.,extended haplotypes) (data not shown). PHASE haplotype blocks andhaplotype block frequencies for all chromosomes are shown in Table 9.The number of common haplotypes accounting for >80% of the observedchromosomes varied among the three blocks. We identified 5 commonhaplotypes for both blocks 1 and 2 and a total of 7 common haplotypesfor block 3. For block 3, the number of common haplotypes also variedamong sample groups. The haplotype distributions for each block differedsignificantly among the examined groups (Table 9).

The distribution of the 5 block 1-haplotypes amongst controls, PTENmutation negative patients, mutation positive patients and variationpositive patients was significantly different (χ²=30.66;P-value=0.0098). Haplotype 1 was found to be under-represented in PTENmutation negative samples (49.7%) and over-represented in the controlpopulation (63.8%). Haplotype 2 was over-represented in PTEN mutationnegative and PTEN mutation positive samples compared to both control andPTEN variation positive samples, 18.2% and 16.5% versus 12.2% and 12.3%,respectively. Interestingly, the percentage of low frequency haplotypeswas also over-represented among both PTEN mutation negative and PTENvariation positive samples (10.3% and 8.8%, respectively) compared tocontrols (2.7%).

Statistically significant differences were also observed for thehaplotype distributions of blocks 2 and 3 between the examined samplepopulations (χ²=45.31 and 62.53, respectively; P-values<0.0001 for bothcomparisons). For block 2, haplotype 1 was under-represented in both thePTEN mutation negative samples (19.2%) and the PTEN mutation positivesamples (21.4%) compared to control subjects (29.3%). Haplotype 2 wasthe most frequent haplotype among the PTEN variation positive samples(32.4%) and over-represented in this group compared to both the controland PTEN mutation negative samples (15.4% and 16.4%, respectively). Theconverse was observed for haplotype 4; a 9.8% haplotype frequency wasseen in the PTEN variation positive samples compared to 21.3% and 20.2%for the control and PTEN mutation negative samples, respectively.

As observed for block 1, low frequency haplotypes were alsoover-represented in PHTS samples. These haplotypes were over-representedin both PTEN mutation negative and PTEN mutation positive samplescompared to controls for block 2: 8.9% and 9.2% versus 3.7%. For block3, low frequency haplotypes are only represented in the three PHTSsample groups (2.7% in PTEN mutation negative samples, 2.4% in PTENmutation positive samples, and 5.4% in PTEN variation positive samples).

Block 3-haplotype 2 was under-represented in PTEN variation positivesamples (9.8%) and over-represented in the control (21.3%) and PTENmutation negative populations (20.5%). As discussed above for block2-haplotypes 2 and 4 among these same three sample populations, block3-haplotype 6 also displayed an inverse relationship with block3-haplotype 2: PTEN variation positive samples (19.1%) compared to thecontrol (6.9%) and PTEN mutation negative (6.5%) samples. Thisobservation suggests that a founder haplotype is formed by the extendedhaplotype between blocks 2 and 3 (haplotypes 4 and 2, respectively).Furthermore, an extended haplotype may also exist between block2-haplotype 2 and block 3-haplotype 6, however, the former appears to beassociated with more haplotype diversity (see Table 10).

To explore genetic associations pertaining to extended haplotypes, wealso reconstructed haplotypes spanning all 30 SNPs (Table 10). 10extended haplotypes represented 81.9% of all haplotypes observed in ourcohort, while 71 additional ‘rare’ extended haplotypes accounted for theremaining 18.1% (data not shown). Statistically significant differenceswere observed between the sample populations (χ²=77.64; P-value=0.0001).Haplotype 2 was observed to be under-represented in both the PTENmutation negative (8.6%) and PTEN mutation positive (8.7%) samples. Thissame haplotype was over-represented in the PTEN variation positivesamples (18.6%). Haplotype 5 was over-represented in the controlpopulation, 13.8%, and under-represented in both the PTEN mutationnegative and PTEN variation positive groups, 7.5% and 5.9% respectively.Interestingly, extended haplotype 1, the most frequent haplotypeobserved in all sampled chromosomes (16.0%), was under-represented inPTEN variation positive samples (9.3%) compared to both control (18.6%)and PTEN mutation negative (19.2%) samples. This haplotype is comprisedof block 2-haplotype 4 and block 3-haplotype 2, as well as block1-haplotype 1 (the most common haplotype observed in this block, 50% inall sample populations). This strongly suggests that, despite thepresence of two recombination hot-spots, a founder haplotype likelyexists for this region of 10q. Two additional extended haplotypes, 2 and5, were also observed to be over-represented in the control population(13.3% and 13.8%, respectively) compared to the PTEN mutation negativegroup (8.6% and 7.5%, respectively). Haplotype 2 was alsounder-represented in PTEN mutation positive samples (8.7%).

Additionally, as observed for each of the three individual blocks, thefrequencies of ‘rare’ extended haplotypes were different among thedifferent sample populations, accounting for only 12.8% of controlchromosomes, compared to 22.6% and 18.6% of PTEN mutation negative andPTEN variation positive chromosomes, respectively. These data suggestthat rare alleles may underlie the disease etiology in these samplepopulations and, more specifically in the case of the PTEN mutationnegative group, may harbor pathogenic variant(s) which escaped detectionby ‘standard’ PTEN mutation scanning methodologies.

To examine these associations further, a series of comparative haplotypeanalyses among PI ITS and control samples for haplotype blocks and theextended haplotypes were examined (see Table 11). A significantdifference was observed for block 1 between the PTEN mutation negativeand control samples (χ²=18.20; P-value=0.0027) (Table 11). For PTENvariation positive samples, block 2, block 3, and the extended haplotypeall differed significantly from the control population (χ²=22.06;P-value=0.0005, χ²=37.96; P-value=<0.0001, and χ²=38.84;P-value=<0.0001, respectively). Notably, the allele frequencies ofseveral individual SNPs comprising these haplotype blocks weresignificantly different among these same two groups (Table 8 and FIG.8). A comparison among PTEN mutation negative and PTEN variationpositive samples revealed significant differences at these same genomicregions: block 2 (χ²=28.65; P-value=<0.0001), block 3 (χ²=39.97;P-value=<0.0001), and the extended haplotype (χ²=44.13;P-value=<0.0001). In a comparison based on stratification by clinicaldiagnoses (Table 11), block 2, block 3 and the extended haplotype werealso associated with CS-like patients, reaching statistical significancefor each of these comparisons (χ²=18.46; P-value=<0.0024, (χ²=24.35;P-value=<0.0010, (χ²=28.02; P-value=<0.0018, respectively). A similartrend was observed for this phenotype when the PTEN mutation negativeand PTEN mutation positive groups were combined (block 2: (χ²=13.60;P-value=<0.0587, block 3: (χ²=12.61; P-value=<0.0273, and the extendedhaplotype: (χ²=21.81; P-value=<0.0095) (Table 11). While interesting,only the comparison of the extended haplotype was statisticallysignificant. Additionally, among PTEN mutation negative and PTENmutation positive CS patients, block 1 appeared to show an associationwith this phenotype (χ²=14.16; P-value=<0.0146), although tis result didnot reach statistical significance following Bonferroni adjustment

Discussion

PHTS represents an assemblage of phenotypically diverse syndromesmanifested by germline pathogenic mutations in the PTEN gene. Standardgermline mutation scanning has identified causal variants in a majorityof patients diagnosed with this complex disorder, particularly forpatients diagnosed with CS or BRRS (Eng, C., Hum. Mutat, 22:183-198(2003); Pilarski, R., et al., J. Med. Genet., 41:323-326 (2004)).Despite extensive mutation scanning, however, the etiologic variant(s)have yet to be identified in 15% and 35% of patients with thesesyndromes, respectively. To investigate genetic associations with PTENin this subset of patients, as well as to characterize the haplotypearchitecture of this locus, a case-control haplotype-based approach wasutilized.

Similar approaches have been used to examine genetic associations at agrowing number of candidate genes (Drysdale, C. M., et al., Proc. Natl.Acad. Sci. USA., 97:10483-10488 (2000); Greenwood, T. A., et al.,Genomics, 82:511-520 (2003); Yu, C., et al., Cancer Res., 64:7622-7628(2004)). Haplotype-based approaches are of particular interest as mostreports of disease-associated mutations describe variants that directlyalter the protein coding sequence of a gene. These studies fail toconsider other mechanisms that may alter gene function and, wheremutations are not found, may overlook polymorphisms that reside outsideof the coding region. Such mechanisms include alterations of generegulation through the disruption of trans-acting factor(s) andcis-acting sequence element interactions, resulting in a pathologicstate (Kleinjan, D. A., et al., Am. J. Hum. Genet., 76:8-32 (2005)).

While the mutation spectrum of PTEN in PHTS has been well studied, itshaplotype architecture has not. The extent of LD across this regions hasbeen examined in three previous studies (Haiman, C. A., et al., CancerEpidemiol Biomarkers Prev., 15:1021-1025 (2006); Hamilton, J. A., etal., Br. J. Cancer, 82:1671-1676 (2000); Zhang, L., et al., Am. J. Med.Genet. B Neuropsychiatr. Genet., 141:10-14 (2006)). Hamilton et al.first reported the existence of two distinct four-marker haplotypes inthe general population, but found no association with prostate cancerand benign prostatic hyperplasia (Hamilton, J. A., et al., Br. J.Cancer, 82:1671-1676 (2000)). A study by Zhang et al. examined theassociation of this same locus with smoking initiation and nicotineaddiction using 5 haplotype tagging SNPs (htSNPs) selected using theSNPbrowser software program (Applied Biosystems, Forster City, Calif.)(Zhang, L., et al., Am. J. Med. Genet. B Neuropsychiatr. Genet.,141:10-14 (2006)). In this study, three haplotype blocks were observed;block 1 spanned 41 kb (from nucleotide position 89,606,485 to89,647,130), block 2 spanned 16 kb (from nucleotide position 89,679,301to 89,695,409), and block 3 included a single SNP located at position89,716,724. As the authors noted, this differed slightly from the PTENhaplotype structure observed by the International HapMap Project. Mostrecently, Haiman et al. investigated the influence of common variationsacross this region and the risk of sporadic breast and prostate cancer(Haiman, C. A., et al., Cancer Epidemiol Biomarkers Prev., 15:1021-1025(2006)). Also employing a htSNP approach, these authors identified 9common haplotypes representing >87% of all chromosomes across 123 kb ofthe PTEN locus. Among these common haplotypes, no strong association wasfound with either sporadic cancer.

For the present study, haplotype phase was reconstructed for samplesusing the SNPHap software program, based on pair-wise LD measurementsand the EM algorithm (Excoffier, L., et al., Mol. Biol, Evol.,12:921-927 (1995); Lewontin, R. C., Genetics, 50:757-782 (1964)).Previous studies have demonstrated the appropriateness of the EMalgorithm for inferring haplotypes from data obtained from unrelatedindividuals (Excoffier, L., et al., Mol. Biol. Evol., 12:921-927 (1995);Bonnen, P. E., et al., Genome Res., 12:1846-1853 (2002); Niu, T., etal., Am. J. Hum. Genet., 70:157-169 (2002); Tishkoff, S. A., et al., Am.J. Hum, Genet., 67:518-522 (2000)). Because the analysis describedherein relied on statistical inferences of haplotypes from unphaseddata, this reconstruction was validated using a second algorithm basedon a Bayesian approach as implemented in the PHASE software program(Stephens, M., et al., Am. J. Hum. Genet., 68:978-989 (2001); Stephens,M., et al., Am. J. Hum. Genet., 73:1162-1169 (2003)) Although the twoprograms rely on different mathematical approaches, both algorithmsperformed remarkably similarly.

The analysis of the LD structure across this region of 10q revealedthree distinct haplotype blocks; block 1 spans 33 kb (from nucleotideposition 89,583,605 to 89,616,359), block 2 spans 65 kb (from nucleotideposition 89,629,942 to 89,694,699), and block 3 spans 43 kb (fromnucleotide position 89,702,453 to 89,745,623). Block 2 is flanked byregions of decreased LD, suggesting that SNPs at these sites lie withinareas of chromosome recombination. The block partitioning, based on themethod by Gabriel et al., partially agreed with that described by Zhanget al. However, based on the data, herein block 1 described by Zhang etal. is actually made up of two distinct blocks. As previously mentioned,these authors defined this region using two htSNPs. To ensure theaccurate characterization of this region, it was decided to empiricallyassess its haplotype architecture using a high-density set ofpolymorphic markers. Because the extent of LD is variable in thisregion, the htSNP approach failed to capture all pertinent informationregarding the locus in question, specifically regarding the breakdown ofLD observed at SNP10 (IVS1−14725delG) and SNP22 (IVS5−2459 T/C).Therefore, a more dense marker set is required. htSNP approaches arecapable of capturing most haplotype diversity within a population, i.e.,approximately 90% of all chromosomes in a given population (Gabriel, S.B., et al., Science, 296:2225-2229 (2002)). However, for uncommonhaplotypes, particularly in cases where the causal allele isunder-represented, this approach is limited. The finding that ‘rare’haplotype blocks account for 2- to 3-fold more PHTS chromosomes comparedto control chromosomes and ‘rare’ extended haplotypes account for nearly2-fold more PTEN mutation negative and PTEN variation positivechromosomes, indicates that for rare diseases, such as PHTS, lowfrequency, or ‘rare’, haplotypes are the ones associated with diseaseand may harbor pathogenic variants.

Herein, in the effort to characterize the haplotype architecture of thePTEN locus, two PHTS patients, 1582-02 and 2849-01, with hemizygousmicro-deletions were identified. Each sample retained only a single copyof the PTEN allele; 1582-02 retained extended haplotype 4 and 2849-01retained extended haplotype 5. These haplotypes had allele frequenciesof 9.9% and 9.2%, respectively, in the entire sample population,resulting in less than a 1% chance of homozygosity for these alleles. Bycontrast, three of the four homozygous control samples were homozygousfor the most frequent haplotype observed in our study. Based on theanalysis of microsatellite markers, these deletions span less thanapproximately 312 kb to 390 kb, respectively (data not shown).Previously, PTEN deletions in only three PHTS patients, all of whom wereclinically diagnosed with BRRS or CS/BRRS overlap were identified (Zhou,X. P., et al., Am. J. Hum. Genet., 73:404-411 (2003)). The patientsidentified in the current study have diagnoses of classic CS (2849-01)and CS-like (1582-02). Implications from these data extend to theclinical realm, indicating that PTEN deletion analysis is warranted inall PHTS patients with CS, BRRS, CS/BRRS, and CS-like phenotypes wholack apparent germline mutations.

Interestingly, one PTEN mutation negative sample was homozygous for a‘rare’ extended haplotype with an allele frequency<0.7% in the entirestudy population. Close inspection of this haplotype revealed thatblocks 2 and 3 were relatively common, while block 1 consisted of a lowfrequency block. This low frequency haplotype block, GACCCTCGI (SEQ IDNO. 19), was only observed in 8 samples; seven PTEN mutation negativesamples and one PTEN variation positive sample. Carriers of this alleleinclude 4 CS patients, 3 CS-like patients, and 1 CS/BRRS patient. Forthe homozygous sample, this indicates that, because of the locations ofour amplicons, the deletion analysis may have been unable to detect apossible deletion of the 5′ region of this locus. This data implicatesthe GACCCTCGI (SEQ ID NO. 19) block as a low frequency, highly penetrantPHTS susceptibility allele. Furthermore, all 8 samples have similar‘rare’ extended haplotypes; 5 (3 CS and 2 CS-like) share the samehaplotype, 1 (CS/BRRS) deviates from this haplotype by a singlevariation in block 2, and 2 (1 CS and 1 CS-like) are variable for bothblocks 2 and 3. Although the SNPs which make up this block and extendedhaplotype are not causal (based on their frequency in the controlpopulation), they are likely in LD with an unknown functional variantconferring disease susceptibility. This further supports the notion that‘rare’, low frequency alleles (LD blocks and/or extended haplotypes) maybe associated with disease and should therefore be considered ascandidate susceptibility alleles in rare disorders.

In addition to an association with rare haplotypes, the analysis ofhaplotype blocks and extended haplotypes revealed significantdifferences among the control group and various patient samplepopulations. The number and frequency of common haplotypes needed tocover >80% of the observed chromosomes varied for each of the threeblocks and the extended haplotype. Similar to the association with rarealleles, these data indicate greater haplotype diversity among the PHTSpatient populations compared to the control group and are indicative ofa higher degree of recombination of the ‘ancient haplotype’.Interestingly, the overall LD pattern observed in the patient samplesappears to indicate the presence of extended haplotypes. This effectseemed most apparent when PTEN variation positive patients were comparedto controls, revealing significant differences between these groups forblocks 2 and 3, as well as for the extended haplotype, and suggestingless recombination among PHTS patients. Furthermore, a pairwisecomparisons between groups revealed that the PTEN mutation negative andPTEN mutation positive groups were most similar, suggesting thatdifferent pathogenic variants may have arisen from similar haplotypicbackgrounds. Taken together, these data indicate that some PHTSpatients, i.e., PTEN mutation positive individuals, and perhaps PTENvariation positive individuals, exhibit a haplotype-founder effect,while others, i.e., PTEN mutation negative individuals, harbor rareextended haplotypes which have undergone extensive ‘shuffling’ of the LDblocks across this region.

Interestingly, among PTEN mutation negative samples, the strongestgenetic effect appears to be associated with haplotypes forming block 1(a block spanning at least 30 kb upstream of PTEN and which includesseveral kilo-basepairs of the gene's first intron). With the exceptionof PTEN's core promoter and exon 1, this region has not been wellcharacterized. Screening efforts which have failed to identifymutations/variations at these sites in this group of patients suggestthat alterations in this region may have a role in PTEN's regulation.These likely involving novel regulatory elements and contribute to itsderegulation.

Various PHTSs, such as BRRS and CS, appear to be caused by the same PTENmutations, despite clear differences in phenotypic presentation (Eng,C., Hum. Mutat., 22:183-198 (2003)). The R130X mutation in exon 5, forexample, occurs in 8 PTEN mutation positive patients included in thisstudy. Among these individuals, 3 have a clinical diagnosis of CS, 2have a clinical diagnosis of BRRS, and 3 have a clinical diagnosis ofCS/BRRS. Both BRRS individuals are carriers of extended haplotypes 3 and10 and exhibit classic features of BRRS including macrocephaly, lipomas,and pigmented macules of the penis. The probability of this genotype inthe general population is <0.3%, suggesting that this infrequent alleliccombination likely contributes to their phenotype and that low-penetrantfunctional variants reside on these loci. Furthermore, althoughstratification by clinical phenotype was only minimally associated withour haplotypes, correlations from these data become more apparent whenthe patient's mutation status is considered.

In addition to providing a panel of informative markers for testinggenetic associations at the PTEN locus, the data strongly indicate thatspecific haplotypes along this region are associated with increased PHTSsusceptibility. ‘PTEN mutation negative’ samples lacking traditionalmutations in the PTEN coding sequence possess a significantly differenthaplotype architecture compared to control samples. Along with anassociation to block 1 of this locus, ‘rare’ alleles comprise thisarchitecture and may underlie the disease etiology in these patients.Furthermore, haplotype profiles in PHTS patients with knownmutations/variations contribute to the phenotypic complexity of thissyndrome. Although the mechanisms underlying these relationships haveyet to be elucidated, these data indicate that associated chromosomalsegments likely harbor variants, potentially involved in thetranscriptional regulation of PTEN, which are both pathogenic and/ormodifying in nature, the manifest as low-penetrant diseasesusceptibility alleles.

TABLE 7 Characteristics of 30 SNP panel. Minor Variation Allele(major/minor Fre- SNP dbSNP ID Position^(a) allele) quency^(b)Location^(c) 1 rs7085791 89,583,605 G/T 0.12 −30602 2 rs1088775689,587,630 A/T 0.15 −26577 3 rs10887758 89,593,295 T/C 0.20 −20912 4rs11202585 89,598,759 G/C 0.19 −15448 5 ss52090924^(d) 89,603,299 T/C0.20 −10908 6 rs11202590 89,607,699 C/T 0.14 −6508 7 rs190386089,610,190 T/C 0.13 −4017 8 rs12573787 89,613,696 G/A 0.14 −510 9rs3216482 89,616,359 ins/del A 0.20 IVS1+2074 10 rs11355437 89,629,037del/ins G 0.40 IVS1−14725 11 rs2673836 89,629,942 A/G 0.29 IVS1−13820 12ss52090925^(d) 89,634,206 C/G 0.21 IVS1−9556 13 rs10887763 89,645,216A/G 0.14 IVS2+1370 14 rs3831732 89,645,229 ins/del A 0.39 IVS2+1382 15rs12569872 89,655,492 G/A 0.14 IVS2+11645 16 rs1234224 89,665,276 A/G0.32 IVS2−9974 17 ss52090926^(d) 89,666,296 del/ins 32nt 0.39 IVS2−895418 rs10490920 89,675,623 T/C 0.14 IVS3+329 19 rs3830675 89,680,936ins/del 0.31 IVS4+109 TCTTA 20 ss52090927^(d) 89,689,289 del/ins 16nt0.15 IVS5+6300 21 rs2299941 89,694,699 A/G 0.12 IVS5−7156 22ss52090928^(d) 89,699,396 T/C 0.21 IVS5−2459 23 rs2673832 89,702,453 A/G0.14 IVS6+457 24 ss52090929^(d) 89,710,231 T/C 0.22 IVS7−400 25 rs55589589,710,887 T/G 0.31 IVS8+32 26 rs926091 89,711,392 C/T 0.14 IVS8+537 27rs701848 89,716,725 T/C 0.39 *614 28 rs10509532 89,727,534 C/T 0.14*12325 29 rs7908337 89,743,671 T/C 0.24 *28462 30 rs11202614 89,745,623C/T 0.14 *30414 ^(a)SNP position on chromosome 10, Mar. 2006 HumanGenome assembly, NCBI Build 36.1, (hg18) ^(b)Frequency in controlpopulation ^(c)Location relative to translation start codon (−), PTENexons (IVS), or translation stop codon (*) ^(d)SNPs identified by DNAresequencing in our screening set

TABLE 8 Summary of SNP allele frequency data for control sample and PHTSpatient populations. Allele SNP n Frequency P-value  1 G T Ctrl 94 0.880.12 — Mut− 146 0.81 0.19 0.0739 Mut+ 103 0.81 0.19 0.0920 Var+ 102 0.870.13 0.8844  2 A T Ctrl 94 0.85 0.15 — Mut− 146 0.76 0.24 0.0219 Mut+103 0.74 0.26 0.0083 Var+ 102 0.79 0.21 0.1807  3 T C Ctrl 94 0.80 0.20— Mut− 146 0.78 0.22 0.6749 Mut+ 103 0.80 0.20 0.9607 Var+ 102 0.79 0.210.9735  4 G C Ctrl 94 0.81 0.19 — Mut− 146 0.78 0.22 0.4491 Mut+ 1030.80 0.20 0.7522 Var+ 102 0.82 0.18 0.9062  5 T C Ctrl 94 0.80 0.20 —Mut− 146 0.78 0.22 0.6368 Mut+ 103 0.80 0.20 0.9431 Var+ 102 0.79 0.210.8278  6 C T Ctrl 94 0.86 0.14 — Mut− 146 0.84 0.16 0.5202 Mut+ 1030.84 0.16 0.6405 Var+ 102 0.88 0.12 0.7544  7 T C Ctrl 94 0.86 0.14 —Mut− 146 0.84 0.16 0.5202 Mut+ 103 0.84 0.16 0.6405 Var+ 102 0.88 0.120.6450  8 G A Ctrl 94 0.86 0.14 — Mut− 146 0.83 0.17 0.5615 Mut+ 1030.83 0.17 0.6542 Var+ 102 0.87 0.13 0.8598  9 D I Ctrl 94 0.80 0.20 —Mut− 146 0.78 0.22 0.6368 Mut+ 103 0.80 0.20 0.9607 Var+ 102 0.78 0.220.6498 10 D I Ctrl 94 0.60 0.40 — Mut− 146 0.54 0.46 0.2033 Mut+ 1030.62 0.38 0.7570 Var+ 102 0.73 0.27 0.0091 11 A G Ctrl 94 0.70 0.30 —Mut− 146 0.79 0.21 0.0351 Mut+ 103 0.78 0.22 0.0914 Var+ 102 0.74 0.260.5368 12 G C Ctrl 94 0.79 0.21 — Mut− 146 0.79 0.21 0.9388 Mut+ 1030.83 0.17 0.3401 Var+ 102 0.90 0.10 0.0026 13 A G Ctrl 94 0.86 0.14 —Mut− 146 0.84 0.16 0.7762 Mut+ 103 0.83 0.17 0.6542 Var+ 102 0.90 0.100.2832 14 I D Ctrl 94 0.61 0.39 — Mut− 146 0.54 0.46 0.1526 Mut+ 1030.62 0.38 0.9257 Var+ 102 0.74 0.26 0.0090 15 G A Ctrl 94 0.86 0.14 —Mut− 146 0.84 0.16 0.7762 Mut+ 103 0.84 0.16 0.7512 Var+ 102 0.89 0.110.445 16 A G Ctrl 94 0.69 0.31 — Mut− 146 0.66 0.34 0.5814 Mut+ 103 0.620.38 0.2137 Var+ 102 0.56 0.44 0.0127 17 D I Ctrl 94 0.61 0.39 — Mut−146 0.54 0.46 0.1526 Mut+ 103 0.62 0.38 0.9257 Var+ 102 0.74 0.26 0.012318 T C Ctrl 94 0.86 0.14 — Mut− 146 0.83 0.17 0.5615 Mut+ 103 0.84 0.160.7512 Var+ 102 0.88 0.12 0.5397 19 D I Ctrl 94 0.69 0.31 — Mut− 1460.66 0.34 0.5814 Mut+ 103 0.64 0.36 0.3446 Var+ 102 0.55 0.45 0.0073 20I D Ctrl 94 0.85 0.15 — Mut− 146 0.84 0.16 0.9743 Mut+ 103 0.84 0.160.9815 Var+ 102 0.89 0.11 0.2885 21 A G Ctrl 94 0.87 0.13 — Mut− 1460.88 0.12 0.9112 Mut+ 103 0.86 0.14 0.9259 Var+ 102 0.89 0.11 0.6513 22C T Ctrl 94 0.79 0.21 — Mut− 146 0.79 0.21 0.9893 Mut+ 103 0.84 0.160.2256 Var+ 102 0.87 0.13 0.0340 23 G A Ctrl 94 0.86 0.14 — Mut− 1460.91 0.09 0.1572 Mut+ 103 0.88 0.12 0.6188 Var+ 102 0.93 0.07 0.0538 24C T Ctrl 94 0.79 0.21 — Mut− 146 0.79 0.21 0.9893 Mut+ 103 0.84 0.160.2256 Var+ 102 0.90 0.10 0.0026 25 T G Ctrl 94 0.69 0.31 — Mut− 1460.65 0.35 0.5299 Mut+ 103 0.63 0.37 0.2961 Var+ 102 0.54 0.46 0.0054 26C T Ctrl 94 0.86 0.14 — Mut− 146 0.83 0.17 0.5615 Mut+ 103 0.84 0.160.7512 Var+ 102 0.88 0.12 0.5397 27 T C Ctrl 94 0.61 0.39 — Mut− 1460.55 0.45 0.2260 Mut+ 103 0.63 0.37 0.8474 Var+ 102 0.74 0.26 0.0090 28C T Ctrl 94 0.86 0.14 — Mut− 146 0.83 0.17 0.5615 Mut+ 103 0.84 0.160.7512 Var+ 102 0.89 0.11 0.4450 29 T C Ctrl 94 0.76 0.24 — Mut− 1460.72 0.28 0.4920 Mut+ 103 0.73 0.27 0.6173 Var+ 102 0.75 0.25 0.9071 30C T Ctrl 94 0.86 0.14 — Mut− 146 0.84 0.16 0.6292 Mut+ 103 0.84 0.160.7512 Var+ 102 0.85 0.15 0.9282

TABLE 9 Haplotype blocks across the PTEN locus. A. Block 1 Controls PTENPTEN PTEN Haplotypes (n = 188)^(a) Mutation − (n = 292)^(a) Mutation +(n = 206)^(a) Variation + (n = 204)^(a) 1 GATGTCTGD 0.638 0.497 0.5490.559 (SEQ ID NO. 20) 2 TTTGTCTGD 0.122 0.182 0.165 0.123(SEQ ID NO. 21) 3 GACCCTCAI 0.138 0.120 0.141 0.108 (SEQ ID NO. 22) 4GTTGTCTGD 0.027 0.055 0.073 0.074 (SEQ ID NO. 23) 5 GACCCCTGI 0.0480.045 0.044 0.049 (SEQ ID NO. 19) Low Frequency 0.027 0.103 0.029 0.088χ² statistic 30.66 P-value 0.0098 B. Block 2 Controls PTEN PTEN PTENHaplotypes (n = 188)^(a) Mutation − (n = 292)^(a) Mutation + (n =206)^(a) Variation + (n = 204)^(a) 1 GCADGAITDIA 0.293 0.192 0.214 0.255(SEQ ID NO. 24) 2 ACADGGITIIA 0.154 0.164 0.199 0.324 (SEQ ID NO. 25) 3ACAIGADTDIA 0.176 0.240 0.204 0.162 (SEQ ID NO. 26) 4 AGAIGADTDIA 0.2130.202 0.165 0.098 (SEQ ID NO. 27) 5 ACGDAGICIDG 0.128 0.113 0.126 0.103(SEQ ID NO. 28) Low Frequency 0.037 0.089 0.092 0.059 χ² statistic 45.31P-value <0.0001 C. Block 3 Controls PTEN PTEN PTEN Haplotypes (n =188)^(a) Mutation − (n = 292)^(a) Mutation + (n = 206)^(a) Variation +(n = 204)^(a) 1 ATTCCCTC 0.176 0.226 0.214 0.157 (SEQ ID NO. 29) 2ACTCCCTC 0.213 0.205 0.160 0.098 (SEQ ID NO. 30) 3 ATTCTCTC 0.160 0.1230.136 0.216 (SEQ ID NO. 31) 4 ATGTTTCT 0.144 0.154 0.150 0.098(SEQ ID NO. 32) 5 ATGCTCTC 0.101 0.110 0.107 0.118 (SEQ ID NO. 33) 6ATGCTCTC 0.069 0.065 0.097 0.191 (SEQ ID NO. 34) 7 GTTCTCTC 0.138 0.0890.117 0.069 (SEQ ID NO. 35) Low Frequency 0.000 0.027 0.024 0.054χ² statistic 62.53 P-value <0.0001 ^(a)n = Number of Haplotypes

TABLE 10 Extended haplotypes for all 30 SNPs across the PTEN locus. PTENPTEN PTEN PTEN Total Controls Mutation − Mutation + Variation +Extended Haplotypes (n = 890)^(a) (n = 188)^(a) (n = 292)^(a) (n =206)^(a) (n = 204)^(a)  1 GATGTCTGDDAGAIGADTDIACACTCCCTC 0.160 0.1860.192 0.155 0.093 (SEQ ID NO. 36)  2 GATGTCTGDIGCADGAITDIATATTCTCTC0.119 0.133 0.086 0.087 0.186 (SEQ ID NO. 37)  3TTTGTCTGDDACAIGADTDIATATTCCCTC 0.113 0.101 0.137 0.121 0.083(SEQ ID NO. 38)  4 GACCCTCAIIACGDAGICIDGTATGTTTCT 0.099 0.117 0.0820.117 0.088 (SEQ ID NO. 39)  5 GATGTCTGDIGCADGAITDIATGTTCTCTC 0.0920.138 0.075 0.107 0.059 (SEQ ID NO. 40)  6GATGTCTGDIACADGGITIIATATGCTCTC 0.064 0.027 0.031 0.073 0.137(SEQ ID NO. 41)  7 GATGTCTGDIACADGGITIIATATGCTCTC 0.054 0.048 0.0550.063 0.049 (SEQ ID NO. 42)  8 GACCCCTGIIACADGGITIIATATGCTCCC 0.0440.048 0.038 0.044 0.049 (SEQ ID NO. 43)  9GATGTCTGDDACAIGADTDIATATTCCCTC 0.039 0.059 0.048 0.029 0.020(SEQ ID NO. 44) 10 GTTGTCTGDDACAIGADTDIATATTCCCTC 0.035 0.016 0.0310.044 0.049 (SEQ ID NO. 45) Low Frequency 0.181 0.128 0.226 0.16 0.186χ² statistic 77.64 P-value <0.0001 ^(a)n = Number of Haplotypes

TABLES 11 Comparative Haplotype Analysis. Extended Block 1 Block 2 Block3 Haplotype χ² χ² χ² χ² Comparison statistic P-value statistic P-valuestatistic P-value statistic P-value PTEN Mutation − 18.20 0.0027 12.030.0614 10.44 0.1649 17.27 0.0447 vs. Ctrl PTEN Mutation + 6.78 0.23769.66 0.0854 8.67 0.2771 13.34 0.2054 vs. Ctrl PTEN Variation + 12.340.0304 22.06 0.0005 37.96 <0.0001 38.84 <0.0001 vs. Ctrl PTEN Mutation −10.91 0.0531 3.41 0.7566 3.83 0.7987 13.05 0.2899 vs. PTEN Mutation +PTEN Mutation − 5.02 0.5415 28.65 <0.0001 39.97 <0.0001 44.13 <0.0001vs. PTEN Variation + PTEN Mutation + 8.38 0.1364 13.82 0.0318 21.650.0029 20.31 0.0161 vs. PTEN Variation + Clinical Features: 9.32 0.316229.76 0.0193 26.42 0.0484 7.98 0.0924 Overall CS vs. Ctrl 12.36 0.03027.61 0.1788 10.08 0.1841 15.51 0.1147 BRRS vs. Ctrl 1.57 0.6667 9.870.0789 10.03 0.1233 5.07 0.4065 CS/BRRS vs. Ctrl 1.87 0.3932 9.19 0.05641.31 0.8600 0.49 0.4825 CS-like vs. Ctrl 12.94 0.0240 18.46 0.0024 24.350.0010 28.02 0.0018 PTEN Mut. − and + 8.82 0.0659 13.41 0.0984 23.700.0220 3.58 0.4700 Clinical Features: Overall PTEN 14.16 0.0146 12.400.0883 10.12 0.0720 11.98 0.1519 Mut. − and + CS vs. Ctrl PTEN 0.960.8107 9.04 0.1715 9.02 0.1083 4.11 0.5339 Mut. − and + BRRS vs. CtrlPTEN 1.70 0.4027 0.04 0.8415 4.32 0.1155 0.29 0.5890 Mut. − and +CS/BRRS vs. Ctrl PTEN 11.35 0.0449 13.60 0.0587 12.61 0.0273 21.810.0095 Mut. − and + CS- like vs. Ctrl Note: PHTS patients werestratified based on their PTEN mutation status and compared to controls,as well as each other. The Bonferroni-adjusted nominal significancelevel used for this comparison was P-value <0.0083. Significant resultsare indicated in bold. Note: An overall Comparison was made based onstratification of clinical features followed by comparisons based onclinical diagnoses (CS, BRRS, CS/BRRS, or CS-like) for all patientsamples, irrespective of their mutation status, and compared tocontrols. The Bonferroni-adjusted nominal significance level used forthis comparison was P-value <0.0125. Significant results are indicatedin bold. Note: Overall comparisons of patient clinical diagnoses amongPTEN mutation negative and PTEN mutation positive samples were performedfollowed by comparisons made among this group versus control samplesbased on patient clinical diagnosis. The Bonferroni-adjusted nominalsignificance level used for this comparison was P-value <0.0125.Significant results are indicated in bold.

SUPPLEMENTAL TABLE 1Primer sequences and genotyping methodologies for all SNP andquantitative real-time PCR reactions Genotyping SNP Forward PrimerReverse Primer Methodology  1 GATAGAGTCTTGCTCTGTAG ACCATACAATATCTGCCTTGSNaPshot (SEQ ID NO. 46) (SEQ ID NO. 47)SBE primer: tgccacgtcgtgaaagtctgacaaGAGTAGCTGGGACTACAG (SEQ ID NO. 48) 2 GCTGTGGTATGTACTTTCTG ATGCATGAAACAGCTACTTG RFLP(BanI) (SEQ ID NO. 49)(SEQ ID NO. 50)  3 TAAGTGGATCATGCCTGTAG CTTAATGGATGCAGACTCAGRFLP (BsiIIKAI) (SEQ ID NO. 51) (SEQ ID NO. 52)  4 CATTCTCAAGCAGGACTCAGAATCCACCTGCTTCAGCTTC RFLP (HincII) (SEQ ID NO. 53) (SEQ ID NO. 54)  5ACTGCAACTTTGACCTCCTG GCAGAATCTCACTCTGTCAG RFLP(DpnII) (SEQ ID NO. 55)(SEQ ID NO. 56)  6 GCTGTGGTTGCTCATCATTC CAATAGGAAGATACCCTGAC RFLP (AciI)(SEQ ID NO. 57) (SEQ ID NO. 58)  7 CCTGATGTTTAGACAAGCAGCTTAGATTGCTGATCTTGTCTCC RFLP(BfaI) (SEQ ID NO. 59) (SEQ ID NO. 60)  8ACTGGGCATGCTCAGTAGAG AGACCAACTCTCCGGCGTTC DNA resequencing(SEQ ID NO. 61) (SEQ ID NO. 62)  9 TTACTAAGGCTAAACTGGAC /FAM/-Fragment Analysis (SEQ ID NO. 63) gcgaatcGTCATGTCACAGCTCACATG(SEQ ID NO. 64) 10 GGATCACAGATGTAGGCTTG /FAM/- Fragment Analysis(SEQ ID NO. 65) catcgccTAGCTGAGAGTGTACTAGAC (SEQ ID NO. 66) 11AGTTGAGAAGTCTAGTACAC ATCCTGTAATCCCACTCTAG SNaPshot (SEQ ID NO. 67)(SEQ ID NO. 68)SBE primer: atcgagatcgacccacaatccactggtcCTATAGTTGTGAATATGTTTAT(SEQ ID NO. 69) 12 GCAAGATAGCTAGTACCATG AATGCCATATGCTAGCACAGRFLP (MboII) (SEQ ID NO. 70) (SEQ ID NO. 71) 13 AGGAATTCATGTCTGATGTGGTGACTGTACTGCTCACTTC SNaPshot (SEQ ID NO. 72) (SEQ ID NO. 73)SBE primer: gtgcAATCAATTTTTGTACCTACAAA (SEQ ID NO. 74) 14 /HEX/-TAAACAGTCCTTCTGGCATC Fragment Analysis cgtccgaCATTATGCAGATGTAGACTC(SEQ ID NO. 76) (SEQ ID NO. 75) 15 TAGCATATTCTGACTCCTTCGATTAGCCCAAGAGTTGTAC SNaPshot (SEQ ID NO. 77) (SEQ ID NO. 78)SBE primer: agtcttcgagatccagccatcatcgactggtcAGTGCTGGGATTATAGGC(SEQ ID NO. 79) 16 TGTAACCTGCAGGAGGCATC AAAGCAGAGAGGTAATACTC SNaPshot(SEQ ID NO. 80) (SEQ ID NO. 81) SBE primer: attacgtaGACTACGACCCAGGTAGG(SEQ ID NO. 82) 17 ACAGTTGTTCACAGTGGTAG /FAM/- Fragment Analysis(SEQ ID NO. 83) gtaccgtTCCTAAGCAGATTGCTCCTG (SEQ ID NO. 84) 18TGCTTGTTAGAGTGAGGTAG CTAGCTCTATCAATCAGGTG RFLP (NcoI) (SEQ ID NO. 85)(SEQ ID NO. 86) 19 AGGTAGGTATGAATGTACTG /HEX/ Fragment Analysis(SEQ ID NO. 87) agtcgatATCAGACTCCTCTTATCAAC (SEQ ID NO. 88) 20ACTGCAACCTCTACCTCCTG /FAM/- Fragment Analysis (SEQ ID NO. 89)cgtccgcAGCTCAATGAACTCATGTAC (SEQ ID NO. 90) 21 GCAACTGAATAGATGCGTAGATAACTAACACCATCGTCAC 26 SNaPshot (SEQ ID NO. 91) (SEQ ID NO. 92)SBE primer: cttaatccgtagtcaCCATTACTTCACCTCATCT (SEQ ID NO. 93) 22GGTACACTACTAATCACTTG TCACCGTGTTAGCCAGGATG RFLP (DraI) (SEQ ID NO. 94)(SEQ ID NO. 95) 23 GGAAGACTAGGTATTGACAG AAAGAGCATCAATGAGACTCRFLP (NlaIII) (SEQ ID NO. 96) (SEQ ID NO. 97) 24 AGAAACTGGAGCTTCTCATGAAGGCAATCTGAGTTATCTG RFLP (HpyCH4IV) (SEQ ID NO. 98) (SEQ ID NO. 99) 25AAGACAAAGCCAACCGATACTT GGAAAGACTAGAAGAGGCAGAAGC RFLP (HincII)(SEQ ID NO. 100) (SEQ ID NO. 101) 26 Same as SNP25 Same as SNP25RFLP (BsaXI) 27 CATAATACCTGCTGTGGATG TCAGACCACAGCTAGTGAAC SNaPshot(SEQ ID NO. 102) (SEQ ID NO. 103)SBE primer: aagctaggtgccacgacgagatagtctgagaaCCGAGTTGGGACTAGGGC(SEQ ID NO. 104) 28 ATTGCTTCGCTCACCTGCTC CCTTTGAGATCCTCAGTAAGRFLP (HpyCH4IV) (SEQ ID NO. 105) (SEQ ID NO. 106) 29TAATTCTGGAGCTTCCTGAG CTGACTCTATACTCTGTGAG SNaPshot (SEQ ID NO. 107)(SEQ ID NO. 108)SBE primer: atctagatccacccatactccgactatcAGGCTGAGGCATGAGAAT(SEQ ID NO. 109) 30 TTGGCTACAAATGTCTCTAG GGTGCTGCTGTTTACTGAGRFLP (Bsu36 I) (SEQ ID NO. 110) (SEQ ID NO. 111)Quantitative Real-time PCR Primers GAPDH exon GTATCGTGGAAGGACTCATGGGAAATTATGGGAAAGCCAG 7 (SEQ ID NO. 112) (SEQ ID NO. 113) PTEN exon 2GTTTGATTGCTGCATATTTCAG CCTGTATACGCCTTCAAGTC (SEQ ID NO. 114)(SEQ ID NO. 115) PTEN exon 5 CGAACTGGTGTAATGATATG TCCAGGAAGAGGAAAGGAAA(SEQ ID NO. 116) (SEQ ID NO. 117) SBE Primer = Single base extensionprimer used in SNaPshot assay. Lower-case indicates non-homologous tail.

Example 3 Targets for Use in Prognosis and Therapy of Head and NeckSquamous Cell Carcinomas (HNSCC)

Methods

HNSCC Samples

A total of 122 consecutively obtained formalin-fixed, paraffin-embedded,primary squamous cell carcinomas of the head and neck (HNSCC) from 122patients, who have not been previously treated and who had not been on aclinical trial, have been analyzed in this study (Table 13). Of these,63 (53.4%) were pharyngeal carcinoma and 55 (46.6%) were oral squamouscell carcinoma (mainly lingual carcinomas). In addition, 1 laryngealcancer and 2 carcinomas of unknown primary were analyzed. Among thepharyngeal SCC, 38.1% (n=24) were located in the oro-pharynx and theremaining (n=39) in the hypo-pharynx. The distribution according to pTNMclassification was as follows: 20.9% T1, 40% T2, 17.27% T3 and 21.8% T4,which is similar to that obtained for all corners at academicinstitutions. The clinical staging followed the guidelines by theAmerican Joint Committee of Cancer (6th edition) (Table 13). The study,which utilized anonymized unlinked samples, was approved, under exemptstatus, by the participating institutional Review Boards for HumanSubjects' Protection. Examination of Cancer Registry informationrevealed that the subjects happened to have been smokers.

LCM and DNA Extraction

Laser capture microdissection (LCM) was performed using the ArcturusPixCell II microscope (Arcturus Engineering Inc., Mountain View, Calif.)in order to isolate the two compartments of the neoplastic tissue(epithelium and stroma) separately (FIGS. 11A, 11B) (Fukino, K., et al.,Cancer Res., 64:7231-7236 (2004)). Specifically captured were stromalfibroblasts adjacent to malignant epithelium (i.e., the tumor stroma)under direct microscopic observation. These stromal fibroblasts residedeither in between aggregations of epithelial tumor cells or no more than0.5 cm distant from a tumor nodule. Corresponding normal DNA for eachcase was procured from normal tissue (preferentially tumor negativelymph node), obtained from a different tissue block containing onlynormal tissue.

Genome Wide Loss of Heterozygosity/Allelic Imbalance (LOH/AI) Scan

Genomic DNA was extracted as previously described by us in detail(Weber, F., et al., Am. J. Hum. Genet. J., 78(6):961-72 (2006); Fukino,K., et al., Cancer Res., 64:7231-7236 (2004)). Polymerase chain reaction(PCR) was performed using DNA from each compartment (normal control,tumor epithelium and tumor stroma) of each sample and one of 72multiplex primer panels, which comprises 366 fluorescent labeledmicrosatellite markers. Genomic location is based on the MapPairs®genome-wide Human Markers set (version 10) (Invitrogen, CA) developed atthe Marshfield Institute. This whole genome panel has an average 16.2markers per chromosome (ranging from 7 to 29 markers per chromosome) orapproximately a 9cM inter-marker distance. Genotyping was performed withthe ABI 377×1 or 3700 semi-automated sequencer (Applied Biosystems,Perkin-Elmer Corp., Norwalk, Conn.). The results were analyzed byautomated fluorescence detection using the GeneScan collection andanalysis software (GeneScan, ABI). Scoring of LOH/AI was performed bymanual inspection of the GeneScan output (FIG. 11C). A ratio of peakheights of alleles between germline and somatic DNA≧1.5 was used todefine LOH/AI as previously described by us and others (Weber, F., etal., Am. J. Hum. Genet. J., 78(6):961-72 (2006); Marsh, D. J., et al.,Cancer Res., 57:500-503 (1997); Nelson, H. H., et al., Carcinogenesis,26:1770-1773 (2005); Dacic, S., et al., Am. J. Surg. Pathol., 29:897-902(2005)). As described previously, the methodological veracity of LOH/AIusing Multiplex-PCR on archived tissue was extensively validated (Weber,F., et al., Am. J. Hum. Genet. J., 78(6):961-72 (2006); Fukino, K., etal., Cancer Res., 64:7231-7236 (2004)).

Statistical Analysis

In total, 366 microsatellite markers were analyzed in both epitheliumand stroma samples from the 122 patients. First, regional LOH“hot-spots”, defined as a significantly higher frequency of LOH at amarker or markers compared to other markers along the same chromosome,were determined. Towards those ends, for each marker, the statisticalsignificance of overall (across all samples) LOH frequency compared tothe chromosome average was analyzed using the exact test of binomialproportions (R base package binom.test.; http://www.r-project.org).Second, the association of LOH/ROH in epithelium and stroma samples withpresenting clinico-pathologic parameters such as location, pT, pN,grade, clinical stage, age and sex, were analyzed using a binomial modelwith nested structures (McCullagh, P., et al., Generalized LinearModels: Chapman and Hall; 1983; Faraway, J. J., Extending Linear Modelswith R: Generalized Linear, Mixed Effects and Nonparametric RegressionModels: Chapman and Hall; 2006). Of note, the age was dichotomized into2 classes using age of 40 years as the cutoff. For associations withclinical stage, pT or pN, the statistical significance was tested usingthe test of trend for multiple proportions. Multiple testing adjustmenthas been applied by using False Positive Report Probability (FPRP)(Wacholder, S., et al., J. Natl. Cancer Inst., 96:434-42 (2004)) with aprior probability of 0.05 and 0.01, denotated as FPRP_(0.05) andFPRP_(0.01), respectively. FPRP indicates the probability that astatistically significant finding is a false-positive by consideringthree factors: the p-value magnitude, the statistical power, and theprior probability of true associations. Only those with p-values<0.05and estimated FPRP values less than 50% (or P<0.5), indicating a smallprobability of being a false positive, are reported as statisticallysignificant findings. For example, a significant value with a priorprobability of 0.01 and an FPRP value less than 50% is denotedFPRP_(0.01)<0.5. The hierarchical clustering and pattern visualizationwere performed using PfCluster (Xu, Y., et al., PfCluster: a new clusteranalysis procedure for gene expression profiles. Paper presented at: AConference on Nonparametric Inference and Probability with Applicationsto Science (Honoring Michael Woodroofe), Ann Arbor, Mich., 2005). The Rpackage (http://www.r-project.org) was used for the data mining andstatistical analysis.

Results

The study described herein included predominantly (97.5%) squamous cellcarcinoma (SCC) of the oral cavity and pharynx of patients with ahistory of smoking. Overall, 244 test samples (122 epithelium and 122stroma samples compared against 122 corresponding normal tissue of 122patients) were analyzed for genomic instability using 366 microsatellitemarkers. LOH/AI is called in stroma or epithelium when the genotypingdata at each marker is compared to data from the corresponding normaltissue from each subject. In total, 43,591 informative (non-homozygous)data points were obtained. Of these, 28,320 markers (65%) showed loss ofheterozygosity/allelic imbalance (LOH/AI) and 15,271 markers (35%)retained heterozygosity (ROH). There was no difference in the number ofinformative markers between the stroma and epithelium (48.4% vs. 48.9%).For the epithelium, the frequency of LOH/AI per sample was 69.0%(ranging from 33.3 to 93.7%) compared to an LOH/AI frequency of 64.4%(ranging from 25.8% to 90.3%) observed in the stroma (p=0.10). In orderto confirm that the high frequency of LOH/AI observed in the stroma isnot a result of epithelial contamination, a multi-level approach wastaken to provide conclusive evidence against an erroneous or artifactualfinding (FIG. 11A-11C). First, for several cases, markers with opposingLOH/AI calls in each compartment of a given tumor (ie. LOH/AI observedin the epithelium but not stroma, and vise versa) were noted. Second, insome cases with concordant LOH/AI calls, it was found that differentalleles are lost in a compartment-specific manner. Third, somaticmutations in some of these cases that were confined to either theepithelium or stroma but not in both were identified (data not shown).Since all analyses have been performed from the same pool of extractedDNA, such observations exclude to a very high probability thepossibility of tissue admixture or inter-compartmental contamination.

Validating Previous Loci of Allelic Imbalance Associated with HNSCCOncogenesis

As a control, the samples were examined for compartment-specific LOH/AIin the markers residing in the previously reported regions of LOH/AI on3p, 9p and 17p with LOH frequencies>50% in “whole” or epithelium-onlyHNSCC. In this study, “strong” hot-spots of LOH/AI were observed in themicrodissected tumor epithelium for two distinct regions on chromosome3. The first chromosome 3 hot-spot maps to sub-band p25.2-25.3 (Tables14 and 15). The second 3p hotspot maps to 3p14.2 (D3S1766) and is evenmore significantly associated with stroma (Table 14). The stroma alsohad this same hot-spot mapping to sub-band p25.2, and perhaps a broaderregion defined by markers D3S2432 and D3S2409 (Table 16). Among allloci, chromosome 9 harbored the second highest frequency of LOH/AI (95%)for the epithelium at 9p21.3-p23 (84% to 95%, Data not shown).Interestingly, in this study, besides a hot-spot at 17p13.1-p13.3 (TP53locus), a hot-spot of LOH/AI was noticed at 17p13.3 (D17S1308),telomeric of the TP53 locus (Table 16). Of the 27 loci with the mostsignificant LOH/AI in the epithelial component, 11 have been reported byother groups to harbor regional losses by CGH (Bockmuhl, U., et al.,Head Neck, 20:145-51 (1998); Bockmuhl, U., et al., Genes Chrom. Cancer,33:29-35 (2002); Huang, Q., et al., Genes Chrom. Cancer, 34:224-33(2002)). Thus, of the previously reported regions of LOH/AI, all wereidentified in our compartment-specific study, and served as a positivecontrol.

Novel HNSCC Compartment-Related Hot-Spots of Genomic Alterations

Hot-spots are defined as markers that show a significantly higherfrequency of LOH/AI compared to all other loci on the same chromosome.In total, 70 hot-spots (at p<0.05 and FPRP_(0.05)<0.5) were identified,17 occurring only in the epithelium, 43 only in the stroma and 10 inboth epithelium and stroma (Tables 14, 15, and 16). The most significanthot-spot (p<0.001; FPRP_(0.05)<0.5) of LOH/AI observed exclusively inthe epithelium was defined by D165422 mapping to 16q23.3 (Table 15).Eight additional highly significant hot-spots of genomic instability(p<0.01) were identified at 1q31.1 (D1S518), 1q43 (D1S1594), 3q13.3(D3S2460), 15q25.3 (D15S655), 16p13.3 (D16S1616), 20p12.2 (D20S851),21q22.2 (D21S2055) and 3p25.2 (D3S4545, see above) [Table 15]. Among the43 hot-spots of LOH/AI that were restricted to the stroma, 30 loci werehighly significant (p<0.01, FPRP_(0.05)<0.5, Table 16). Highest rankedamong these were D17S1308 (17p13.3) and D14S1434 (14q32.13) followed byD10S1230 (10q26), D2S1400 (2p25.2) and D2S1790 (2p11.2)[Table 16]. Whilethe data showed that hot-spots of LOH/AI are more diverse in the tumorstroma than in the epithelium (43 vs. 17, p=0.005) of HNSCC, thefrequency of highly significant loci among all hot-spots within eachcompartment was similar (9 out of 17 and 30 out of 43, p=0.56).

Besides the two hot-spots of LOH/AI at D3S1766 and D3S2403 mentionedabove (“Validating previous loci of genomic alterations in HNSCConcogenesis”), genomic alterations at 14q13.3 (D14S606) and 12q24.32(D12S2078) was found most frequently in both epithelium (p=0.0029 and0.0011) and stroma (p=0.00043 and 0.013) [Table 14]. Furthermore, anadditional 8 loci were identified as non-compartment specific hot-spotsof LOH/AI (ie, occurring equally in both epithelium and stroma) with acut-off at p<0.05 and FPRP_(0.05)<0.5 (Table 14). One locus thatretained heterozygosity (ie, did not show genomic instability) at afrequency higher than what would be expected by chance was alsoidentified: D14S599, representing chromosome sub-band 14q13.1, showedLOH/AI only in 16 out of 58 informative samples (27.6%, p<0.000001) inthe epithelium and 16 out of 57 (28.1%, p<0.000001) in the stromalcompartment.

The data mining process described herein allowed the identification lociof LOH/AI that extended over 2 or more adjacent hot-spot markers,indicating larger regions of genomic alterations on chromosome arms 3p,12q and 14q. For instance, 12q24.32 (D12S2078) harbored a hot-spot ofLOH/AI for the epithelium (81.2%, p=0.0012) and stroma (75.0%, p=0.013).A second hot-spot region on chromosome 12 was located at 12q13.13(D12S297) affecting only stroma (80.3%, p=0.0009) and extends furthercentromeric, to 12q21.33 (D12S1294) [74.3%, p=0.014] and to 12q24.23(D12S395) (77.9%, p=0.0019). In addition, LOH/AI at 11q12.1 (D11S4459)was identified in 84.6% of the stroma (p=0.0021) samples. Similarassociations, but with presenting clinico-pathologic features, arefurther explored in the next section below.

Association of LOH/AI with Presenting Clinico-Pathologic Parameters

Data mining was then performed on the whole-genome LOH/AI scan to inorder to identify compartment-specific loci that show a correlationbetween LOH/AI frequency and clinico-pathologic parameters.Interestingly, stromal-specific LOH/AI-clinico-pathologic correlationswere more frequently observed than for the epithelium. First, we soughtto identify LOH/AI at loci that were positively associated withaggressiveness of disease as reflected by clinical stage, grade, pT andpN status (FIG. 12, Table 17). It was found that LOH/AI at D6S305 (6q26)in the epithelium occurred significantly more frequently in clinicalstage III/IV HNSCC (88.6%) than in stage I/II tumors (58.3%, p=0.011)(Table 17). In addition, a linear increase of LOH/AI frequencies fromstage I (50%) and stage II (63%) to stage III (80%) and to stage IV(95%) tumors (p=0.011) was observed for the locus 6q26 which containsthe common fragile site FRA6E. No such association with clinical stagewas identified for LOH/AI in the stroma. Interestingly, LOH/AI atD4S2417 (4q34.3) in the stroma showed a positive correlation withincreasing pT stage (p=0.00085) (FIG. 12, Table 17). Furthermore,markers mapping to D3S3630 and D19S599 (3p26.3, p=0.012; 19q13.31,p=0.017) showed an increasing frequency of LOH/AI correlating with thedegree of lymph node involvement (Table 17). For the epithelium-specificLOH/AI, genomic alterations identified at 18p11.22 (D18S843) werepositively correlated with regional lymph node metastasis (pN) with 33%LOU/AI in NO tumors compared to 79.4% in lymph node positive disease[p=0.00092]. Importantly, no positive correlation between LOH/AI in theepithelium and pT stage was observed.

The mucosa of the upper aero-digestive tract is exposed to an array ofcarcinogens that have been attributed to cause genetic and epigeneticchanges in the squamous cell lining and ultimately lead to HNSCCgenesis. It is evident that these carcinogens not only affect theseepithelial cells but also the mesenchymal fibroblasts, the latterrepresenting the largest component of the stroma. With this study it isshown for the first time, that indeed the stromal cells in HNSCC aresubjected to selection for locus-specific LOH/AI events. The highfrequency of LOH/AI especially in the tumor stroma might appeardistracting at first. However, it does reflect the biological backgroundbehind HNSCC since in the study only patients with a history of smokinghave been analyzed. In addition, technical aspects have to be consideredas well. First, it is important to note our operational definition of ahot-spot, which is defined as a locus having a significantly highfrequency of LOH/AI compared to all other loci along the samechromosome. Thus, it is possible that other studies using a small set ofmarkers might therefore find an apparently high frequency of LOH/AI inone marker and labeled this locus significant; however, other loci alongthe same chromosome, which may not have been examined, might actuallyhave LOH/AI to a similar or even elevated degree than the selectedmarker. In addition, studies using array comparative genomichybridization (aCGH), while having the advantage of differentiatingbetween allelic gain and loss, usually detect losses/gains of largergenomic regions, spanning several BAC clones. In contrast,microsatellite marker LOH analysis is able to accurately identifysubmicroscopic deletions or even single base-pair alterations, if thoseaffect the microsatellite marker priming sites. However, it is importantto recognize that in this study, the common observation of “earlyevents” (ie. those with high frequency of LOH/AI) attributed to HNSCConcogenesis that are loss at 3p, 9p and 17p in the tumor epithelium(Table 14) could be recapitulated. This acts as a control that the datamining approach described herein can correctly identifycompartment-specific hot-spots of genomic instability in microdissectedepithelium and, more importantly, the stroma of HNSCC lesions.

Multiplicity of LOH/AI Hot-Spots in the Stroma of HNSCC

Interestingly, more LOH/AI hot-spots were observed in the stroma thanepithelium. Even where the same LOH/AI hot-spot markers were found inboth the epithelium and stroma, overall, the frequencies of LOH/AI weremuch higher in the corresponding stroma (Table 14). This may indicatethat only a very limited set of key genetic alterations within theepithelium are required to initiate HNSCC genesis and other alterationsare downstream events or even bystander events. This has been addressedpreviously by Gotte et al. who reports on the intratumoral heterogeneityof HNSCC (Gotte, K., et al., Adv. Otorhinolaryngol., 62:38-48 (2005)).In contrast, the multiplicity of stroma-specific hot-spots, likelyoccurring along all steps of carcinogenesis, indicate that these playthe fundamental role in influencing the biological diversity, and hence,clinical behavior, of the disease (FIG. 12, see next sections). Whetherthe accumulation of stromal alterations occurs concordant with theneoplastic transformation of the epithelium or in fact precedes themalignant transformation of the squamous epithelium is unknown. Inbreast cancers from individuals with germline BRCA1/2 mutations, theinherited dysfunction in these repair genes seems to dictate thatstromal genomic alterations occur before or at least simultaneously withepithelial transformation (Weber, F., et al., Am. J. Hum. Genet. J.,78(6):961-72 (2006)).

Besides several genes involved in oncogenesis or cell-cell communicationmapping to these hot-spots, micro-RNA's that might become deregulatedthrough allelic imbalance were also found. It is becoming in emergingconcept that the deregulation of micro-RNA's participate not only indevelopment but also cancer. For instance hsa-miR-181 (19p13.12) wasidentified as a stroma-specific hot-spots, and has been implicated incellular differentiation through regulation of homeobox genes(Naguibneva, I., et al., Nat. Cell. Biol., 8(3):278-84 (2006)). Giventhat hot-spot and LOH/AI frequencies highest in stroma, it is likelythat if field cancerization precedes invasive HNSCC, then themesenchymal cells undergo genetic alterations first.

Evidently, the positively selected stromal cells acquire additionalhits, presenting as multiple hot-spots of LOH/AI, that can lead toaberrant excretion of proteins and misinterpretation of incoming signalsresulting in disruption of the physiologic interplay between epitheliumand stroma and provides the necessary microenvironment to sustain andpromote tumor progression (Mueller, M. M., Nat. Rev. Cancer, 4:839-49(2004); McCawley, L. J., et al., Curr. Biol., 11:R25-7 (2001); Bhowmick,N. A., et al., Nature, 432(7015):332-7 (2004)). Seemingly paradoxically,however, one locus mapping to 14q13.1 retained heterozygosity at asignificant frequency in both epithelium and stroma, indicating thatgenes mapping to those loci might be necessary for maintenance of cellintegrity or key regulatory genes might be frequently affected bysomatic sequence variants that will cause a dominant negative actingtranscripts. Interestingly, among the genes within this region is PHD3(prolyl hydroxylase domains 3; equivalent to EGLN3) involved in oxygensensing and regulation of especially HIF-2α (Appelhoff, R. J., et al.,J. Biol. Chem., 279(37):38458-65 (2004)).

LOH/AI at 5 Markers in the Stroma and 2 in the Epithelium Correlate withPresenting Clinico-Pathologic Features

As described herein, 5 specific loci of LOH/Ai associated withclinico-pathologic features at presentation were found (FIG. 12).Amongst all the hotspot loci associated with presentingclinico-pathologic features, these specific 5 were identified withsequentially increasing LOH/AI frequencies significantly associated withincreasing pT, pN and/or clinical stage and with a low likelihood ofrepresenting false positive associations. Interestingly, 3 specific locioccurred in the stroma, associated with tumoral attributes of aggressivedisease and invasion, namely, size (pT status; 1 locus at 4q34.3) andregional lymph node status (pN, 2 loci at 3p26.3 and 19q13.31). One genein the 4q34.3 region is NEIL3 which encodes a class of glycolases whichinitiate the first step in base excision repair. One therefore couldpostulate that loss of NEIL3 could be one of the first events leading toa cascade of genomic alterations in the stroma (Rosenquist, R. A., etal., DNA Repair, 2:581-91 (2003)).

It does also appear that the stroma plays an important role inmetastases where 2 of the 3 hot-spot loci, at 3p26.3 and to 19q13.31, inthe stroma are correlated with increasing pN status (FIG. 12). There arelikely several genes mapping to these regions. One relevant gene mappingto 3p26.3 is FANCD2 which encodes one of the enzymes in the Fanconianemia (FA) pathway pivotal to DNA repair and which interacts with BRCA1and BRCA2 (Taniguichi, T., et al., Blood, 7:2414-20 (2002); Hussain, S.,et al., Hum. Mol. Genet., 13:1241-8 (2004)). Interestingly, the FApathway is again targeted by the loss of a gene encoding FAZF on19q13.3, the other stromal locus whose loss is associated with pNstatus. This zinc finger protein binds to another FA pathway memberFANCC in a region that is deleted in FA patients with a severe diseasephenotype (Hoatlin, M. E., et al., Blood, 94:3737-47 (1999); Dai, M. S.,et al., J. Biol. Chem., 277:26327-34 (2002)). This 19q locus is proximalto another DNA repair enzyme gene, ERCC2. ERCC2, or XPD, is an excisionrepair enzyme which has been identified to have an increased risk ofcancer when mutated, due to abrogation of its transcriptional activationof FBP, a regulator of MYC (Dai, M. S., et al., J. Biol. Chem.,277:26327-34 (2002)). The observations herein, therefore, indicate thatthese genes in concert may play a role in HNSCC and in particular,relevant to regional metastases. It is tantalizing that the mostpromising candidate genes in the regions of loss associated withclinicopathologic features belong to the various repair pathways. Theloss of FANCD2, FAZF, and ERCC2 together could additively and moreseverely result in additive loss of repair capabilities that result in acascade of downstream genomic alterations, leading to genomicinstability resulting in invasion and metastasis. This postulate issupported by the observations herein in the multiplicity of genomicalterations in HNSCC stroma (see above, Tables 14-16). In furthersupport of this hypothesis, a QTL for prostate cancer aggressiveness hasbeen identified in this region by two groups (Witte, J. S., et al., Am.J. Hum. Genet.: 1:92-9 (2000); Slager, S. L., et al., Am. J. Hum.Genet., 3:759-62 (2003)), suggestive that a gene(s) is harbored in thislocation that may also be important in HNSCC aggressiveness, as ourassociation of this locus to pN suggests. Equally significant is thelocus reflected by D18S843 (18p11.2) in the epithelium. Allelic loss forthis region has previously been implicated in other solid tumors andeven associated with relapse in breast cancer (Climent, J., et al.,Clin. Cancer Res., 8(12):3863-9 (2002); Tran, Y., et al., Oncogene,17(26):3499-505 (1998)). From the genes mapping to this loci it isunclear what the likely candidate will be; of note is APCDD1 withsuggested oncogenic properties in colorectal cancer. Importantly, thisgene is expressed during development to regulate epithelial-mesenchymalinteraction (Jukkola, T., et al., Gene Expr. Patterns, 4(6):755-762(2004)). Only a single specific locus (D6S305) was independentlyidentified as a hot-spot of LOH/AI associated with clinical stage.Deletions of 6q26 (D6S305) have been reported to have a role incarcinogenesis before. This region harbors the common fragile site FRA6Ethat spans 8 genes (IGF2R, SLC22A1, SLC22A2, SLC22A3, PLG, LPA, MAP3K4,and PARK2), which have been implicated in the development of solidcancer (Denison, S. R., et al., Genes Chromosomes Cancer, 38(1):40-52(2003)).

Conclusions

The observations described herein indicate that the apparentlynon-malignant stroma of HNSCC is rich in genomic alterations. The strongassociation of a limited number of specific loci with sequentiallyhigher frequencies of LOH/AI in the stroma with clinical aggressivenessindicates that mesenchyme is affected by carcinogens to the same extentas the squamous cell epithelium, and even more importantly, contributesin a fundamental way to the clinical phenotype of HNSCC. The datadescribed herein indicate that this genetically altered mesenchymalfield might provide the soil which facilitates the HNSCC invasion andmetastases. It is likely that these genomic observations, which point togenomic regions which likely harbor many genes, will guide futurein-depth functional and mechanistic studies. Nonetheless, the presentobservations provide new biomarkers for prediction of clinical outcomeand novel compartments for targeted therapy and prevention.

TABLE 13 Patient Characteristic Characteristic Number Frequency Sex male86 71.1% female 35 28.9% Age mean 58.5 years (+/− 12.9 years) Primarysite Oral 55 46.6% Pharynx 63 53.4% Stage I 16 14.5% II 22 20.0% III 3430.9% IV 38 34.5% pT T1 23 20.9% T2 44 40.0% T3/4 43 39.1% pN N0 4439.3% N1 24 21.4% N2 39 34.8% N3 5 4.5% Grade Low G1, 2 83 80.6% High G320 19.4%

TABLE 14 Hot-Spots of LOH/AI in both Epithelium and Stroma EpitheliumStroma Marker Loci p-value^(a) p-value^(a) Genes D1S1596 1p32.1 0.000260.0000001 JUN, HOOK, CYP2J2 D3S1766 3p14.2 0.014 0.00047 FLNB, PDHB,hsa-mir425, hsa-mir191 D3S2403 3p25.2 0.0013 0.013 CAV3, RAD18, CAMK1,FANCD2, VHL, PPARG, RAF1, HDAC11, FBLN2, WNT7A D6S305 6q26 0.024 0.022IGF2R, MAP3K4, MAS1, PLG, SLC22A1 D12S2078 12q24.32 0.0012 0.013 TMEM132D14S599 14q13.3 0.000001^(b) 0.000005^(b) RGLN3, SNX6, CFL2, BAZ1AD14S606 14q31.1 0.0029^(b) 0.00043^(b) TSHR, GTF2A1, STON2 D17S218017q21.32 0.0054 0.0013 SCAP1, HOXB1-9, IGFLBP1 D19S591 19p13.30.000018^(b) 0.000005^(b) GADD45, ZNF77, TLE2, AES D21S1437 21q21.10.0063^(b) 0.00008^(b) NCAM2 ^(a)Multiple testing adjustment is based onFPRP_(0.05) < 0.5. ^(b)Multiple testing adjustment is based onFPRP_(0.01) < 0.5.

TABLE 15 Hot-Spots of LOH/AI in Epithelium EPITHELIUM Marker Locip-value^(a) Genes D1S518 1q31.1 0.0070 PRG4, TPR, PTGS2, PLA2G4A D1S15941q43 0.008 FMN2, GREM2 D3S4545 3p25.2 0.0076 FANCD2, VHL, PPARG, RAF1,HDAC11, FBLN2, WNT7A D3S2460 3q13.3 0.0053 LSAMP, IGSF11 D5S1462 5q150.0149 LNPEP, LIX1 D8S1128 8q24.21 0.015 MYC D10S1423 10p12.31 0.0193PLXDC2 D11S1999 11p15.4 0.013 ADM, CTR9, GALNTL4 D13S796 13q33.3 0.011EFNB2, LIG4, ABHD13, TNFSF13B D15S655 15q25.3 0.0046^(b) DET1,hsa-mir-7-2, hsa-mir-9-3 D16S2616 16p13.3 0.009 DNAJA3, A2BP1 D16S42216q23.3 0.0002^(b) HSD17B2, CDH13, HSBP1 GATA178F11 18p11.32 0.023 TGIFD18S1376 18q11.2 0.015 CDH2, RBBP8, CABLES, D20S851 20p12.2 0.007 PLCB1,PAK7 D21S2055 21q22.2 0.0039^(b) ETS2, PCP4, DSCAM D22S683 22q12.3 0.018H1F0, POLR2F, PLA2G6 ^(a)Multiple testing adjustment is based onFPRP_(0.05) < 0.5. ^(b)Multiple testing adjustment is based onFPRP_(0.01) < 0.5.

TABLE 16 Hot-Spots of LOH/AI in Stroma STROMA Marker Loci p-value^(a)Genes D1S3721 1p34.2 0.005^(b) CITED, JMJD2 GATA133A08 1p21.1 0.0056VAV3, NBPF4 D2S1400 2p25.1 0.0002^(b) ADAM17, E2F6 D2S1790 2p11.20.00057^(b) POLR1A D2S1334 2q21.3 0.006^(b) CXCR4, ZRANB3 D2S1776 2q24.30.0081 NOSTRIN D3S2432 3p22.3 0.001^(b) TGFBR2, GPD1L D3S2409 3p21.320.026 COL7A1, RHOA, TRAIP, TUSC2, RASSFIA D3S1262 3q27.2 0.017 CRYGS,AHSG, KNG1 D3S2418 3q28 0.0088 FGF12, CLDN1 D4S1647 4q23 0.0018^(b)TSPAN5, EIF4E D5S2500 5q11.2 0.0068 PDE4D D5S1725 5q14.3 0.002^(b)TMEM161B, MEF2C D5S820 5q33.3 0.012 TIMD4, SGCD D6S474 6q21 0.001^(b)FOXO3A, PRDM1 D6S1027 6q27 0.0008^(b) SMOC2, THBS2 D7S3061 7q31.30.0019^(b) NDUFA5, ASB15, WASL D7S1804 7q32.3 0.021 hsa-mir-29b-1D7S3070 7q36.1 0.0028^(b) PRKAG2, GALNT11 D8S1477 8p12 0.01 PPP2CB, WRN,NRG1 D10S1208 10p11.21 0.0024^(b) NRP1, PARD3, FZD8 D10S1230 10q26.10.00005^(b) INPP5P, BRWD2 D10S1222 10q26.2 0.0063 MMP21, BCCIP, APAM12D11S4459 11q12.1 0.0021^(b) SSRP1, CTNND1, hsa-mir-130a D11S1998 11q230.0004^(b) DCSAM, FXYD2, IL10RA D11S4464 11q24.1 0.011 HSPA8, LOH11CR2A,PANX3, ESAM, ACVR1, hsa-mir-125b-1, hsa-mir-100 D12S1042 12p11.23 0.015ARNTL2, PTHLH D12S297 12q13.13 0.0009^(b) ACVR1B D12S1294 12q21.33 0.014CEP290, KITLG, DUSP6, D12S395 12q24.23 0.0019^(b) HSPB8, RAB35, MSI1,TRIAP1 D13S787 13q12.12 0.013 TNFRSF19 D13S285 13q34 0.0009^(b) ING1,SOX1, TUBGCP3 D14S1280 14q12 0.012 PRKD1 D14S588 14q24.1 0.015 WDR22,ERH, SFRS5, SMOC1 D14S1434 14q32.13 0.000002^(b) MOAP1, DICER1, VRK1D16S403 16p12.1 0.021 POLR3E, PLK1 D16S3396 16q12.1 0.002 CARD15 D16S51616q23.1 0.013 ADAMTS18, WWOX, MAF D17S1308 17p13.3 0.000002^(b) HIC1,hsa-mir-132, hsa-mir-212 D17S1294 17q11.2 0.004^(b) CCDC55, SLC6A4,hsa-mir-423 D19S714 19p13.12 0.0036^(b) CASP14, NOTCH3, hsa-mir-27a,hsa-mir-23a, hsa-mir-181c,d D20S103 20p13 0.0005^(b) DEFB1287BC1D2,FKBP1A D21S2052 21q21.3 0.00031^(b) JAM2, APP, ADAMTS1,5, hsa-mir-155^(a)Multiple testing adjustment is based on FPRP_(0.05) < 0.5.^(b)Multiple testing adjustment is based on FPRP_(0.01) < 0.5.

TABLE 17 LOH/AI in epithelium associated with clinical stage stage Istage II stage III, IV Marker Loci LOH ROH LOH ROH LOH ROH p-value^(a)D6S305 6q26 2 2 5 3 31 4 0.011 LOH/AI in stroma associated with pT pT1pT2 pT3, 4 Marker Loci LOH ROH LOH ROH LOH ROH p-value^(a) D4S24174q34.3 5 4 10  6 21 0 0.0008 LOH/AI in stroma or epithelium (*)associated with pN pN0 pN1 pN2, 3 Marker Loci LOH ROH LOH ROH LOH ROHp-value^(a) D3S3630 3p26.3 9 17 9 6 18 8 0.0123 D19S559 19q13.31 9 1410  4 15 3 0.0165 D18S843* 18p11.22 5 10 7 3 20 4 0.0009^(b) LOH, lossof heterozygosity; ROH, retention of heterozygosity; ^(a)Multipletesting adjustment is based on FPRP_(0.05) < 0.5. ^(b)Multiple testingadjustment is based on FPRP_(0.01) < 0.5.

Example 4 Targets for Use in Prognosis and Therapy of Breast Cancer

Materials and Methods

Breast Carcinoma Samples and Laser Capture Microdissection

Two hundred and twenty unrelated samples of primary sporadic invasivecarcinomas of the female breast annotated by basic clinicopathologicfeatures were obtained under the approval of the respectiveInstitutional Review Boards. Samples from males with breast cancer,those with a personal history of ovarian cancer and those with one ormore first degree relatives with breast or ovarian cancer were excluded.Widely metastatic disease (T×N×M1) was also an exclusion criterion.Anonymized sections from archived blocks were linked only to theirrespective clinicopathologic features. No personal identifiers orlinking files were maintained. Laser capture microdissection (LCM) wasperformed using the Arcturus PixCell II microscope (Arcturus EngineeringInc., Mountain View, Calif.) to isolate neoplastic epithelium and tumorstroma separately (Kurose, K., et al., Hum. Mol. Genet., 10:1907-1913(2001); Kurose, K., et al., Nat. Genet., 32:355-357 (2002); Fukino, K.,et al., Cancer Res., 64:7231-7236 (2004); Weber, F., et al., Br. J.Cancer, 92:1922-1926 (2005); Weber, F., et al., Am. J. Hum. Genet.,78:961-972 (2006)). Tumor-associated stromal fibroblasts were collectedfrom locations proximate to epithelial tumor cells, being within 5 mm ofan epithelial tumor nodule. Corresponding germline reference DNA foreach case was procured from normal tissue, either within the breast butat least 1 cm distant from malignant epithelial cells, or fromhistologically normal tissues outside the breast. The different originsof the corresponding germline DNA had no effect on the frequency orpattern of loss of heterozygosity/allelic imbalance (LOH/AI).Photomicrographs of Laser Capture Microdissection of sporadic breastcancer samples.

Laser Capture Microdissection (LCM) was performed on sporadic breastcancer samples stained with hematoxylin and eosin (H&E). The distinctionbetween epithelial and stromal components was very clear under directmicroscopic observation. In order to avoid the cross contamination ofepithelial components into stromal components, epithelial tissues werefirst captured, then surrounding stromal tissues were captured.

Whole Genome Genotyping for LOH/AI

Genomic DNA was extracted as previously described (Kurose, K., et al.,Hum. Mol. Genet., 10:1907-1913 (2001); Kurose, K., et al., Nat. Genet.,32:355-357 (2002)), with incubation in Proteinase K at 65° C. for 2 days(Fukino, K., et al., Cancer Res., 64:7231-7236 (2004)). The primer setsfor multiplex PCR defined 386 microsatellite markers in 72 multiplexpanels (ResGen, Invitrogen, Carlsbad, Calif.). Genotyping was performedwith the ABI 3730 DNA analyzer (Applied Biosystems, Foster City,Calif.). The genotyping results were analyzed by automated fluorescencedetection using the ABI Genemapper v3.5 (Applied Biosystems, FosterCity, Calif.). Scoring of LOH/AI (loss of heterozygosity/allelicimbalance) and ROH (retention of heterozygosity) was done by inspectionof the Genemapper outputs (illustrated in FIGS. 13A-13B). A ratio ofpeak heights of alleles between germ-line and epithelial carcinoma orsurrounding stromal DNA≧1.5 was used to define LOH/AI (Weber, F., etal., Am. J. Hum. Genet., 78:961-972 (2006); Marsh, D., et al., CancerRes., 57:500-503 (1997); Dacic, S., et al., Am. J. Surg. Pathol.,29:897-902 (2005); Nelson, H., et al., Carcinogenesis, 26:1770-1773(2005)). The methodological veracity of LOH/AI using multiplex-PCR onarchived templates was extensively validated as published (Fukino, K.,et al., Cancer Res., 64:7231-7236 (2004)). Three samples were excludedfrom statistical analyses because none of the tested loci wereinformative (all loci homozygous in germline). Statistical analyses wereperformed on the remaining 217 samples, each of which was informativefor at least 79 chromosomal loci. The total number of 386 microsatellitemarkers were used for total genome LOH/AI scan, and each chromosomecontained from 7 (chromosome 21) to 31 (chromosome 1) markers. Standardquality control measures for both LCM-procurement and replicability ofcompartment-specific LOH/AI calls are detailed in our previouspublications, including the comparisons between the results of PCR onthe DNA extracted from LCM-captured tissues and those on the DNA fromthe corresponding frozen tissues, and between the results from multiplexPCR genotyping and those of quantitative PCR and the lack of crosscontamination between compartments (Kurose, K., et al., Hum. Mol.Genet., 10:1907-1913 (2001); Fukino, K., et al., Cancer Res.,64:7231-7236 (2004); Weber, F., et al., Br. J. Cancer, 92:1922-1926(2005); Weber, F., et al., Am. Hum. Genet., 78:961-972 (2006);Ginzinger, D., et al., Cancer Res., 60:5405-5409 (2000); Nigro, J., etal., Am. J. Pathol., 158:1253-1262 (2001)).

Mutation Analysis of TP53

Mutation analysis was performed on the 112 breast cancer samples whichhad informative LOH/AI data at D17S796. Genomic DNA from the epitheliumand stroma from each breast carcinoma was subjected to mutation analysisfor TP53. The classic mutation cluster region of this gene, exons 4-9,exon-intron boundaries and flanking intronic regions of TP53 wereanalyzed by PCR-based direct sequence analysis using the ABI3730x1 aspreviously described (Fukino, K., et al., Cancer Res., 64:7231-7236(2004)). When a mutation was found in the epithelium and/or stroma, thecorresponding germline was examined. No germline TP53 mutations werefound and thus, all mutations found in the breast cancer samples weresomatic.

Clinico-Pathologic Features at Time of Diagnosis

Presenting demographic and clinico-pathologic features included age,tumor grade (modified Scarff-Bloom-Richardson Grades I-III) (Bloom, H.J., et al., Br. J. Cancer, 11:359-377 (1957); Le Doussal, V., et al.,Cancer, 64:1914-1921 (1989); Elston, C., et al., Histopathology,19:403-410 (1991)), tumor size, estrogen- and progesterone-receptor (ERand PR, respectively) expression status, and human epidermal growthfactor receptor 2 (HER2/neu) expression status, as well as primary tumorstatus (pT) and regional lymph node metastasis status (pN) and ClinicalStage Grouping based on the 6th edition of the American Joint Committeeon Cancer (AJCC) Cancer Staging Manual (Greene, F., et al., eds. AJCCCancer Staging Manual, 6th edition. New York: Springer-Verlag; (2002)).For hormone receptor analysis, the percentage of immunoreactive nucleiwas assessed visually and the results were categorized as follows:(+)>10% of nuclei, (+/−)>0% to <10% of nuclei and (−) 0% of nucleiimmunoreactive. In HER2/neu analysis, the results were scored asfollows: (0) no immunoreactivity or immunoreactivity in <10% of tumorcells, (1+) faint weak immunoreactivity in >10% of tumor cells but onlya portion of the membrane is positive, (2+) weak to moderate completemembrane immunoreactivity in >10% of tumor cells, (3+) moderate tostrong complete membrane immunoreactivity in >10% of tumor cells. Scores(0) and (1+) were regarded as negative (−), and (2+) and (3+) aspositive (+), respectively. Cytoplasmic immunoreactivity alone wasscored as a negative result.

Compartment-Specific LOH/AI Profile and Clinico-Pathologic Features:

Analysis of Similarities of LOH/AI Patterns in Epithelium, in Stroma,and Between the Epithelium and the Stroma Derived from the Same SamplesUsing Mcnemar's Test, Hierarchical Clustering and Multi-DimensionalScaling

McNemar tests were performed to compare the LOH/AI between eachcompartment-pair (epithelium and stroma) from each of the tumors and thepooled samples to test whether LOH/AI is more frequent in onecompartment than the other. Dissimilarities between eachcompartment-pair (epithelium and stroma) from each of the tumors can bemeasured by the percentage of discordant pairs of LOH/AI, ie, theproportion of markers showing LOH in one compartment and ROH in theother among all the markers which were informative in both compartments.Multi-dimensional scaling using principal coordinate analysis measuresthe distance between a pair of samples and approximates thedissimilarity between the two as measured by the percentage ofdiscordant LOH/AI. Based on the results for multi-dimensional scaling,two of 217 tumors appeared to have very different LOH/AI patterns fromthose of the rest of the samples. This was most likely due to the smallnumber of informative markers for these two tumors (39 and 46informative markers in epithelium and stroma combined). Therefore, thesetwo samples were excluded from multi-dimensional scaling andhierarchical clustering analyses. Hierarchical clustering with averagelinkage and multi-dimensional scaling was first performed for 430samples, epithelium and stroma separately, derived from the 215 tumors.The clustering was performed using a function in the statistical packageR (used for all statistical analyses in this report and detailed byVenables and Ripley (Venables, W. N., et al., Modern Applied Statisticswith S-Plus, New York: Springer; 1994); Venables, W. N., et al., SProgramming, New York: Springer; 2000)), and the standard option ofaverage linkage was used. As an unsupervised (unbiased) method, genotypewas then correlated with the presenting CPF by repeating the sameanalysis using one clinico-pathologic variable at a time. The sameanalysis was then performed by combining the epithelium and stromasamples from the same tumor to study the overall LOH/AI profile of thetumor.

Associations Between LOH/AI and Clinico-Pathologic Features

Statistical models were applied to study the relationships betweencompartment-specific LOH/AI and clinico-pathologic data. Logisticregression models were used for CPFs with binary features andproportional odds regression models were used for CPFs with more thantwo ordered classes. The covariates in these models are chromosome-wiseLOH/AI frequencies for either compartment (stroma/epithelium) from eachtumor. From these analyses, we obtained a p-value across each chromosomein each compartment and each CPF, representing the strength of evidencefor the correlation between LOH/AI on that particular chromosome in thatcompartment and the CPF. For the group of tests for a specificcompartment and CPF, Bonferroni adjustment was applied to correct formultiple testing. For any association that was statisticallysignificant, Fisher's 2-tailed exact tests were used to associate theCPF with LOH/AI at individual markers on that chromosome in thatcompartment.

Results

Comparisons Between LOH/AI in Epithelium and that in Stroma

Overall, LOH/AI was more frequent in epithelium than in stroma: in theepithelium across all tumors, 43598 PCR reactions were informative forevaluation of LOH/AI and 22288 (51.1%) showed LOH/AI, compared to anoverall 47.6% (18644 out of 39192) in stroma (chi-square p-value2.2×10⁻¹⁶). At the chromosomal level, model-based estimates for theLOH/AI frequency (Fukino, K., et al., Cancer Res., 64:7231-7236 (2004))were significantly higher in epithelium than in stroma for 5 chromosomes(chromosomes 7, 8, 13, 16 and 17) at the 0.05 level (Table 18), andremain so for 3 chromosomes (chromosomes 8, 13 and 17) after Bonferroniadjustment for multiple testing (p<0.05/23).

As proof of concept that regions with significantly high LOH/AI oftenharbor relevant genes, the p13 region of chromosome 17, which harborsthe TP53 tumor suppressor gene, was studied. One of the major regions ofLOH is within 17p13, where LOH at D17S796 (17p13.2) in the epitheliumoccurs in 72 of 112 (64%) informative (ie germline heterozygous at thismarker) breast cancer samples from our series; and in the stroma, 56/106(53%) of informative samples (7 stromal samples failed to amplify).D17S796 is a proximal marker for the TP53 tumor suppressor gene.Therefore, direct mutation analysis was performed by sequencing of theclassic mutation cluster region, exons 4-9 and flanking intronicsequences of TP53 of all epithelial and stromal samples from the 113breast cancers with informative LOH data at this locus. It was foundthat 29 of 112 (27%) tumors had somatic intragenic TP53 mutations in theepithelium and 28 of 106 (26%) had somatic TP53 mutations in the stroma.Only 8 tumors had somatic TP53 mutations in both epithelium and stroma,but for each of these 8 samples, the mutation found in epithelium wasdifferent from that in stroma. Thus, 21 tumors had TP53 mutations onlyin the epithelium and another 20 tumors had somatic mutations only inthe stroma. Of the 30 with TP53 mutations in the epithelium, 80% had LOHat D17S796. Among the 28 with TP53 mutations in the stroma, 65% had LOHat this marker.

Comparison of LOH/AI Profiles Between the Epithelium and the StromaDerived from the Same Samples

The results of the McNemar tests comparing the LOH/AI between theepithelium and stroma samples derived from the 217 subjects indicatesthat for a larger number of subjects, LOH/AI is observed more frequentlyin the epithelium, represented by the positive p-values. This result isconsistent with the overall test, which indicated strong evidence formore frequent LOH/AI in the epithelium (P<0.001). Neithermulti-dimensional scaling or hierarchical clustering revealed any strongsimilarity between LOH/AI profiles for the epithelial or the stromalsamples from the same subject, providing a good control fornon-contamination between compartments (figures not shown). Thehierarchical clustering did result in the samples clusteringprogressively, with the most similar samples clustered together first.

Model of the Association Between Clinico-Pathologic Features and LOH/AI

A two-stage approach was taken to look for associations betweencompartment-specific LOH/AI and CPF's. First, such associations werescreened for at the chromosome-level. The chromosomes that yieldedsignificant correlations were then subjected to analysis at theindividual marker level to determine associations between LOH/AI atspecific markers/loci and the CPF's. For the first stage, therefore,formal model-based methods were applied to examine the correlationsbetween LOH/AI and the presenting CPFs. Compartment-specific LOH/AI datawere used to classify the CPFs using logistic and ordinal regressionmodels, with chromosome-wise LOH/AI as the independent variable, foreach chromosome in turn, and obtained p-values for each presenting CPF.The obtained p-values (Table 19) represent the strength of evidence forthe correlation between LOH/AI on a particular chromosome and theparticular presenting CPF.

Interestingly, more statistically significant (at the 0.05 level, afterBonferroni adjustment) associations with CPFs were found for LOH/AI instroma (7 associations) than that in epithelium (1 association).Specifically, significant associations were found between tumor gradeand LOH/AI on chromosome 11 in stroma (p=0.0013); LOH/AI on chromosome14 in epithelium and PR (p=0.002); and LOH/AI on chromosomes 1(p=0.0006), 2 (p=0.0016), 5 (p=0.0009), 18 (p=0.0009), 20 (p=0.001) and22 (p=0.0002) in stroma and pathologic regional nodal status (pN) (Table19, FIGS. 14A-14B).

Once promising chromosomes were identified, the second stage wasperformed to associate LOH/AI at specific loci and the CPF's. Thus, todetermine if specific markers were responsible for the LOH/AI along thechromosomes noted above that were significantly associated with grade,pN and PR status, Fisher's 2-tailed exact tests were used to test theassociation of the corresponding CPF with LOH/AI at each marker in thecorresponding compartment (Table 19). Markers along chromosome 14 in theepithelium associated with PR status were D14S588 (p=0.029) and D14S1426(p=0.027). Specific markers contributing to the LOH/AI on chromosome 11in the stroma associating with tumor grade were D11S1999 (p=0.00055) andD11S1986 (p=0.042). Importantly, LOH/AI at various markers in the stromawas significantly associated with pN: ATA42G12 (chromosome 1,p=0.00095), D5S1457 (p=0.00095), D5S1501 (p=0.0011), D5S816 (p=0.0008),D18S858 (p=0.0026), D20S103 (p=0.0027), D20S851 (p=0.0045), D22S683(p=0.00033) and D22S1045 (p=0.0013) (Table 19).

Eight significant associations were found between compartment-specific,chromosome-specific LOH/AI and CPFs. While only two markers onchromosome 14 in the epithelium were significantly associated with anyCPF at all, in this case, PR, genomic instability within 7 chromosomesin the stroma of primary invasive breast carcinomas were significantlyassociated with tumor grade (chromosome 11) and the presence of regionallymph node metastases (chromosomes 1, 2, 5, 18, 20 and 22). Previousobservational studies of total genome LOH/AI in breast cancercompartments have also revealed specific regions of chromosome 11 as animportant target of genomic alteration (Kurose, K., et al., Hum. Mol.Genet., 10:1907-1913 (2001); Fukino, K., et al., Cancer Res.,64:7231-7236 (2004)). Furthermore, because this 11q region is also ahotspot of LOH/AI in stroma of head and neck cancers, the role of thisregion in the stroma might be more universal (Weber, F., et al., JAMA,297:187-195 (2007)). ATM is mapped to chromosomal region 11q23.1, thelocus associated with tumor grade. As ATM is responsible for maintaininggenomic integrity (Khanna, K., et al. J. Mammary Gland Biol. Neoplasis,9:247-262 (2004)), it may be postulated that LOH/AI at the ATM locus inthe stroma might initiate general genomic instability in thatcompartment. Lack of ATM has also been shown to be associated withincreased neoangiogenesis and with increased grade and poor clinicaloutcome in non-Hodgkins lymphoma (Cuneo, A., et al., J. Clin. Oncol.,18:2607-2614 (2000)). At least one previous study has shown that LOH/AIat the ATM locus correlated with increased grade in whole (ie withoutcompartment-specific analysis) primary breast carcinomas (Rio, P., etal., Int. J. Oncol., 13:849-853 (1998)). However, one should be awarethat sometimes, genotype-CPF associations may not be as straightforwardas merely reflecting a gene or genes within an identified hot-spot. Forexample, as described herein a model-based statistic was used to lookfor potential associations between compartment-specific presence orabsence of somatic TP53 mutations and specific hot-spot LOH/AI.Interestingly, the presence of somatic TP53 mutations in the stroma, butnot the epithelium, were associated with presence of LOH/AI at our twostroma-specific hotspot markers on chromosome 11 associated with tumorgrade (Patocs A and Eng C, unpublished data). Thus, it is possible thatsomatic mutation of TP53 in the stroma results in genomic instabilityleading to LOH/AI including LOH/AI at 11q23, affecting the ATM locuswhich sets up a perpetuating cycle of increasing genomic instability andhence, high grade tumors.

The association of LOH/AI at 9 specific loci residing on 6 chromosomesin the stroma of primary breast carcinomas with pathologic regionallymph node status is worthy of note. In the process of lymph nodemetastases, there would be at least two rate limiting steps: gainingaccess to the lymphatics at the site of the primary lesion and tumorformation at the regional lymph node (reviewed in Ref. 31) (Schedin, P.,et al., Breast Cancer Res., 6: 93-101 (2004)). For successfulmetastasis, it would seem straightforward that the primary tumor stromashould have many important roles, providing a permissivemicroenvironment that permits invasion. The observation that geneticalterations at the 9 loci (on 6 chromosomes) in the stroma associatingwith pN should reflect the genetically altered microenvironmentfavorable to metastasis. There are two broad categories ofmetastasis-associated genes, ie, metastasis activators and metastasissuppressors (reviewed in Ref. 32) (Debies, M., et al., J. Mammary GlandBiol. Neoplasia, 6:441-451 (2001)). As an example, at least four ofthese are located at those chromosomal loci significantly associatedwith pN in this study, such as Maspin at 18q21.3 (D18S858) (Chen, E., etal., IUBMB Life, 58:25-29 (2006)) (Schedin, P., et al., Breast CancerRes., 6: 93-101 (2004)), EP300 (Krubasik, D., et al., Br. J. Cancer,94:1326-1332 (2006)) at 22q13 (D22S1045), PLCB1 (Cocco, L., et al., Adv.Enzyme Regul., 45:126-135 (2005)) at D20S851 as well or indeed MYH9(Canobbio, I., et al., J Thromb. Haemost., 3:1026-1035 (2005)) onD22S683, which are known to be associated with metastasis and/orinvasion. In particular, EP300 encodes p300 which is a transcriptionalco-factor and prototype histone acetyltransferase which plays a role inmultiple cellular processes. In vitro, p300-deficient cells appeared tohave an aggressive phenotype with loss of cell-cell adhesion and defectsin cell-matrix adhesion (Krubasik, D., et al., Br. J. Cancer,94:1326-1332 (2006)). In vivo, embryos lacking p300 were shown to arrestdevelopment and die between E8.5 and E11, suggesting that p300 would benecessary for normal organ development (Yao, T., et al., Cell,93:361-372 (1998)). The observation herein might also explain why somegroups believe that epigenetic phenomena are more prominent in tumorstroma (Allinen, M., et al., Cancer Cell., 6: 17-32 (2004); Hu, M., etal., Nat. Genet., 37: 899-905 (2005)). However, our current data wouldsuggest that structural loss of such genes as EP300 occur first, withconsequent epigenetic alterations important in tumor stroma occurringthereafter. It is also intriguing that within or close to 7 of the 9pN-associated markers lie genes or loci associated with immunemodulation, eg IL2RB, IBD5 (The AutoImmune Disease Database;http://www.uni-rostock.de/aidb/home.php) and several quantitative traitloci for rheumatoid arthritis (Cornelis, F., et al., Proc. Natl. Acad.Sci. U S A., 95:10746-10750 (1998); Shiozawa, S., et al., Int. Immunol.,10:1891-1895 (1998)). Overall, therefore, the observations describedherein lend evidence that genetic alterations in the tumor stromaactivates/promotes genomic instability and neovascularization (ATM locusLOH/AI and tumor grade) followed by further dysfunction in such genes asEP300 and Maspin whose consequences interact with inflammation andimmune suppressive responses (IL2RB, IBD5, and quantitative trait locifor rheumatoid arthritis) which promotes cell migration and invasion.

These results, therefore, support a model in which genetic changes inboth stromal and epithelial compartments occur during tumorigenesis, andprogression is codetermined by local interaction between these cellpopulations within the primary tumor (Fukino, K., et al., Cancer Res.,64:7231-7236 (2004)). It was previously found that stroma had a greatermultiplicity of genetic alterations than epithelium and the targets ofgenetic alterations in stroma were more numerous and widely distributedthan those in the epithelium. This indicates that epithelium onlyrequires a small number of LOH/AI events to undergo malignanttransformation, but local behavior of the resultant epithelial neoplasmis substantially modified by a broader repertoire of genetic changes inadjacent stroma. The data herein indicate that clinical tumorprogression, as reflected in the measured clinicopathologic features,may be more influenced by locally acquired changes in the stromalenvironment than carcinoma cell genotype per se (Fukino, K., et al.,Cancer Res., 64:7231-7236 (2004)). Stromal genetic changes thatcontribute to clinically relevant outcomes can be mapped to particularchromosomal loci, including two markers on chromosome 11 that correlatewith tumor grade and nine markers on six chromosomes associated withregional lymph node metastasis. Genetic changes acquired in stromaadjacent to transformed epithelial cells contribute an additionaldimension of progression modulation beyond that contributed by thecarcinoma cells themselves. The combination of stromal and epithelialgenetic changes produces a greater range of outcome scenarios than canotherwise be explained by carcinoma cell genotype alone.

The genetics and genomics of tumor stroma from human patients is arelatively new field of exploration compared to the cell biology ofepithelial-stromal interactions in in vitro and non-human solid tumormodels which may date back to 20 or more years. Given the technology ofthe day, albeit modern, there are always caveats to studies such asthis. For example, despite our every care to detail to avoid crosscontamination between compartments, there might be a few stray cellsfrom each. When this occurs, very low level LOH/AI (eg, insubpopulations) will be missed, and so subtle CPF-associations may bemissed. Furthermore, we utilized a 385-marker total genome coverage(10-Mb mean inter-marker distance), and so, it is possible that a fewimportant regions or genes that are relatively distant from each markerwill be missed. For example, the 17q markers closest to the HER2/NEUgene showed a relatively low AI (genomic amplification) frequencycompared to HER2/neu protein expression by immunohistochemistry. It isbelieved that the 386-marker whole genome coverage still did not haveenough resolution to capture the 1 Mb HER2/NEU amplicon as this markerset did not have include a marker within this gene. Added confidence isprovided by similar findings of reproducible genomic, epigenomic andexpressional changes found by different technologies such as CGH andexpression profiling in breast and other carcinomas published by severalother groups (Allinen, M., et al., Cancer Cell., 6: 17-32 (2004); Hu,M., et al., Nat. Genet., 37: 899-905 (2005); Wernert, N., et al.,Anticancer Res., 21:2259-2264 (2001)). Nonetheless, as with anypatient-oriented study, the data can be further validated, perhaps withemerging novel technologies, in larger series especially those withevent-free survival data and therapeutic trials with long follow-up.

TABLE 18 Comparisons between LOH/AI in Neoplastic Epithelium and inStroma at the Chromosome Level p-value of comparison Frequency ofProbability of between LOH/AI LOH/AI Epithelium & Chr. Epithelium StromaEpithelium Stroma Stroma  1 0.507 0.467 0.509 0.49 0.2  2 0.473 0.4760.482 0.505 0.14  3 0.504 0.503 0.502 0.52 0.20  4 0.531 0.497 0.5340.519 0.43  5 0.532 0.485 0.534 0.513 0.27  6 0.526 0.518 0.524 0.5380.43  7 0.477 0.499 0.486 0.53 0.0035*  8 0.553 0.472 0.558 0.502 0.0021 9 0.522 0.504 0.53 0.526 0.84 10 0.508 0.481 0.514 0.513 0.93 11 0.4930.466 0.503 0.489 0.45 12 0.48 0.453 0.481 0.469 0.50 13 0.547 0.4430.562 0.48 0.00010 14 0.541 0.508 0.535 0.511 0.27 15 0.532 0.492 0.5360.516 0.35 16 0.506 0.437 0.51 0.457 0.0043* 17 0.577 0.473 0.581 0.4926.9E−05 18 0.482 0.458 0.493 0.483 0.63 19 0.492 0.469 0.498 0.489 0.6520 0.448 0.429 0.456 0.457 0.99 21 0.497 0.466 0.508 0.493 0.56 22 0.4840.428 0.486 0.44 0.058 X 0.54 0.481 0.539 0.5 0.054 LOH/AI frequencies,model-based estimates and model-based p-values for comparing the LOH/AIfrequencies between 2 compartments on a chromosome basis weresignificantly higher in epithelium than in stroma for 5 chromosomes(chromosomes 7, 8, 13, 16 and 17) at the 0.05 level (* and underlined).After the Bonferroni adjustment to account for multiple testing by using0.05/23 as the significance level, the differences in the LOH/AIestimates between epithelium and stroma were still significant for 3chromosomes (chromosomes 8, 13 and 17, denoted by underline).

TABLE 19 Logistic Regression and Ordinal Regression Models RevealAssociation between Clinicopathological Features andCompartment-Specific LOH/AI at Specific Markers at the Chromosome LevelStage grouping Grade pN Direction/p-value Direction/p-valueDirection/p-value Chr. Epithelium Stroma Epithelium Stroma EpitheliumStroma  1 −1/0.8022   1/0.9238 −1/0.2490 −1/0.0450*   1/0.0151  1/6.00E−04  2   1/0.4911 −1/0.7982 −1/0.2319 −1/0.1097   1/0.0385*  1/0.0016  3   1/0.2726   1/0.5463 −1/0.7756 −1/0.0530   1/0.0608  1/0.0038*  4   1/0.4623 −1/0.9365   1/0.6974 −1/0.1385   1/0.0540  1/0.0061*  5   1/0.6542   1/0.5300 −1/0.5745 −1/0.0175   1/0.0725  1/9.00E−04  6   1/0.1835   1/0.7566 −1/0.9789 −1/0.1893   1/0.0324*  1/0.0089*  7 −1/0.5637 −1/0.8235 −1/0.2397 −1/0.1155   1/0.1005  1/0.0115*  8   1/0.3169   1/0.8170   1/0.3608 −1/0.2009   1/0.0058*  1/0.0025*  9   1/0.3256 −1/0.6632   1/0.9130 −1/0.0927   1/0.0218*  1/0.0192* 10   1/0.1994 −1/0.6173   1/0.3457 −1/0.0098*   1/0.0194*  1/0.0042* 11 −1/0.7846 −1/0.1375 −1/0.7341 −1/0.0013   1/0.2961  1/0.0642 12   1/0.2386 −1/0.5988 −1/0.5833 −1/0.0449*   1/0.0120*  1/0.0085* 13   1/0.7693 −1/0.5967   1/0.8873 −1/0.3289   1/0.0651  1/0.1704 14   1/0.5447   1/0.2882   1/0.0689 −1/0.3861   1/0.3827  1/0.0026* 15   1/0.7068   1/0.9220   1/0.1517 −1/0.2087   1/0.0505  1/0.0031* 16   1/0.5991 −1/0.9970 −1/0.1644 −1/0.9381   1/0.0054*  1/0.0199* 17   1/0.0259*   1/0.5742   1/0.0155* −1/0.2215   1/0.0447*  1/0.0051* 18   1/0.6436 −1/0.9334 −1/0.7684 −1/0.0087*   1/0.0354*  1/9.00E−04 19 −1/0.6225 −1/0.2881   1/0.9831 −1/0.6055   1/0.1298  1/0.0130* 20   1/0.6820   1/0.8607 −1/0.3490 −1/0.0462*   1/0.0679  1/0.0010 21   1/0.6836   1/0.4954 −1/0.4756 −1/0.3321   1/0.0252*  1/0.0039* 22   1/0.3044   1/0.5739 −1/0.0363* −1/0.2581   1/0.0294*  1/2.00E−04 X   1/0.3868 −1/0.4650   1/0.3700 −1/0.3088   1/0.1247  1/0.0173* ER PR HER2/neu Direction/p-value Direction/p-valueDirection/p-value Chr. Epithelium Stroma Epithelium Stroma EpitheliumStroma  1   1/0.3072   1/0.0153* −1/0.9877   1/0.0914   1/0.8663−1/0.8937  2   1/0.2986   1/0.0547   1/0.6011   1/0.2755 −1/0.7121  1/0.7055  3 −1/0.5800   1/0.3036 −1/0.2314   1/0.5035 −1/0.5807  1/0.8085  4 −1/0.4282   1/0.0508 −1/0.7573   1/0.1130 −1/0.3593−1/0.5653  5 −1/0.5874   1/0.0291* −1/0.8853   1/0.2293 −1/0.3775  1/0.4350  6   1/0.2354   1/0.1274   1/0.8118   1/0.4992   1/0.4807−1/0.7607  7   1/0.2164   1/0.0437*   1/0.7783   1/0.2379   1/0.4458−1/0.8926  8   1/0.7421   1/0.1471 −1/0.7201   1/0.5528   1/0.8631−1/0.9428  9 −1/0.5940   1/0.0473* −1/0.6874   1/0.0583 −1/0.1773−1/0.5156 10 −1/0.6192   1/0.0212* −1/0.4450   1/0.2037 −1/0.9040−1/0.9513 11   1/0.5569   1/0.0446* −1/0.9496   1/0.1505   1/0.7484  1/0.5587 12   1/0.9464   1/0.0740 −1/0.4445   1/0.3806 −1/0.9002−1/0.3374 13 −1/0.6319   1/0.2889 −1/0.6118   1/0.6842 −1/0.7068  1/0.1656 14 −1/0.0080*   1/0.9956 −1/0.0020 −1/0.9378 −1/0.2150  1/0.3355 15 −1/0.3094   1/0.0333 −1/0.4018   1/0.2225   1/0.8802  1/0.9123 16   1/0.3688   1/0.2950   1/0.1615   1/0.7380 −1/0.1784  1/0.6331 17 −1/0.0483*   1/0.0504 −1/0.1467   1/0.0857 −1/0.5385  1/0.9803 18   1/0.4614   1/0.0848   1/0.9787   1/0.2617 −1/0.9347−1/0.7319 19 −1/0.8053   1/0.1955 −1/0.1306   1/0.3189 −1/0.6296−1/0.8024 20 −1/0.9375   1/0.1002   1/0.7936   1/0.0646 −1/0.2529  1/0.9487 21   1/0.3919   1/0.0247*   1/0.8128   1/0.1949 −1/0.8748  1/0.3823 22   1/0.4558   1/0.0722   1/0.4166   1/0.1875 −1/0.0095*−1/0.9190 X   1/0.9943   1/0.1985   1/0.7107   1/0.3717 −1/0.5984−1/0.7610 Each cell contains the direction of association (‘1’ or ‘−1’)and a model-based p-value. A positive ‘1’ in Direction indicates thathigher LOH/AI frequencies on the chromosome is related to higher gradeor stage, more lymph node metastasis, positive ER and PR, and so on. Anegative ‘−1’ represents the opposite, ie, inverse relationship. Foreach chromosome, the p-values for both epithelium and stroma are given,with nominally significant results (p ≦ 0.05) denoted with an asterisk*and significant results after Bonferroni adjustment (p ≦ 0.05/23)underlined. Specific Markers Significantly Associated withClinicopathologic Features IN STROMA Frequency of LOH/AI (%) Tumor gradeI II III p-value* D11S1999 50.0 60.9 23.3 0.00055 D11S1986 85.7 51.538.6 0.042 pN 0 1 2 3 ATA42G12 25.9 47.1 100 100 0.00095 D5S1457 28.048.7 100 NI 0.00095 D5S1501 27.0 29.3 85.7 100 0.0011 D5S816 45.7 36.4100 100 0.0008 D18S858 35.9 58.6 100 100 0.0026 D20S103 16.4 21.7 62.5100 0.0027 D20S851 28.8 31.9 77.8 66.7 0.0045 D22S683 42.2 51.5 90.9 NI0.00033 D22S1045 31.3 58.3 87.5 66.7 0.0013 IN EPITHELIUM Frequency ofLOH/AI (%) Progesterone receptor (+) (+/−) (−) p-value* D14S588 32.950.5 57.8 0.029 D14S1426 40.0 62.5 68.3 0.027 *Fisher's 2-tailed ExactTest NI: No informative data available

The teachings of all patents, published applications and referencescited herein and in the provisional application to which priority isclaimed are incorporated by reference in their entirety.

While this invention has been particularly shown and described withreferences to example embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

1. A method of diagnosing breast cancer or susceptibility to breastcancer in an individual comprising detecting in a sample that has beenobtained from the individual a loss of heterozygosity/allelic imbalance(LOH/AI) at one or more loci selected from the group consisting of:D11S1999, D11S1986, D5S1457, D5S1501, D5S816, D18S858, D20S103, D20S851,D22S683, wherein the presence of the LOH/AI at the one or more loci inthe sample of the individual is indicative of a diagnosis of breastcancer in the individual.
 2. The method of claim 1 wherein the one ormore of the loci are present in epithelial tumor cells, stromal cellssurrounding the epithelial tumor cells or a combination thereof.
 3. Themethod of claim 2 wherein the stromal cells are stromal fibroblasts. 4.The method of claim 2 wherein the stromal cells are non-malignant ormalignant.
 5. The method of claim 1 further comprising determiningattributes of the breast cancer.
 6. The method of claim 1 wherein theattributes of the breast cancer are tumor aggressiveness, tumorinvasion, tumor size, regional lymph node status or a combinationthereof.
 7. The method of claim 1 wherein the individual is a human.